Complete Execution Output (JSON):
{
"aml_screening": {
"adverse_media": "no_hits",
"aml_status": "cleared",
"compliance_score": 96.8,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"aml_status\": \"cleared\", \"watchlist_check\": \"no_matches\", \"pep_screening\": \"not_identified\", \"sanctions_check\": \"cleared\", \"adverse_media\": \"no_hits\", \"risk_rating\": \"low\", \"compliance_score\": 96.8, \"screening_date\": \"2024-07-15\", \"manual_review_required\": false}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.261696,
"end_time": "2025-07-15T11:37:15.415159",
"message_sent": true,
"start_time": "2025-07-15T11:37:15.153463",
"timestamp": "2025-07-15T11:37:15.415159",
"worker_executed": true,
"workers_notified": true
},
"manual_review_required": false,
"output": "__OUTPUTS__ {\"aml_status\": \"cleared\", \"watchlist_check\": \"no_matches\", \"pep_screening\": \"not_identified\", \"sanctions_check\": \"cleared\", \"adverse_media\": \"no_hits\", \"risk_rating\": \"low\", \"compliance_score\": 96.8, \"screening_date\": \"2024-07-15\", \"manual_review_required\": false}\n",
"pep_screening": "not_identified",
"return_code": 0,
"risk_rating": "low",
"sanctions_check": "cleared",
"screening_date": "2024-07-15",
"status": "completed",
"stderr": "",
"task_id": "aml_screening",
"watchlist_check": "no_matches"
},
"analyze_bank_statements": {
"account_balance_trend": "stable",
"average_monthly_credits": 7200,
"bounced_transactions": 0,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"average_monthly_credits\": 7200, \"salary_credits_consistent\": true, \"account_balance_trend\": \"stable\", \"minimum_balance\": 15000, \"maximum_balance\": 45000, \"bounced_transactions\": 0, \"loan_emis_detected\": 2, \"total_monthly_debits\": 5800, \"financial_discipline_score\": 85.5, \"irregular_transactions\": false}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.458352,
"end_time": "2025-07-15T11:33:53.479033",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.020681",
"timestamp": "2025-07-15T11:33:53.479033",
"worker_executed": true,
"workers_notified": true
},
"financial_discipline_score": 85.5,
"irregular_transactions": false,
"loan_emis_detected": 2,
"maximum_balance": 45000,
"minimum_balance": 15000,
"output": "__OUTPUTS__ {\"average_monthly_credits\": 7200, \"salary_credits_consistent\": true, \"account_balance_trend\": \"stable\", \"minimum_balance\": 15000, \"maximum_balance\": 45000, \"bounced_transactions\": 0, \"loan_emis_detected\": 2, \"total_monthly_debits\": 5800, \"financial_discipline_score\": 85.5, \"irregular_transactions\": false}\n",
"return_code": 0,
"salary_credits_consistent": true,
"status": "completed",
"stderr": "",
"task_id": "analyze_bank_statements",
"total_monthly_debits": 5800
},
"analyze_credit_history": {
"average_account_age": "32 months",
"credit_limit_utilization": 35.2,
"credit_mix": {
"auto_loans": 1,
"credit_cards": 3,
"home_loans": 0,
"other": 1,
"personal_loans": 1
},
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"oldest_account_age\": \"60 months\", \"average_account_age\": \"32 months\", \"credit_mix\": {\"credit_cards\": 3, \"personal_loans\": 1, \"auto_loans\": 1, \"home_loans\": 0, \"other\": 1}, \"repayment_behavior\": {\"never_missed\": 85.0, \"30_days_late\": 12.0, \"60_days_late\": 2.5, \"90_days_late\": 0.5, \"defaults\": 0.0}, \"credit_limit_utilization\": 35.2, \"recent_credit_behavior\": \"stable\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.336491,
"end_time": "2025-07-15T11:34:00.792280",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.455789",
"timestamp": "2025-07-15T11:34:00.792280",
"worker_executed": true,
"workers_notified": true
},
"oldest_account_age": "60 months",
"output": "__OUTPUTS__ {\"oldest_account_age\": \"60 months\", \"average_account_age\": \"32 months\", \"credit_mix\": {\"credit_cards\": 3, \"personal_loans\": 1, \"auto_loans\": 1, \"home_loans\": 0, \"other\": 1}, \"repayment_behavior\": {\"never_missed\": 85.0, \"30_days_late\": 12.0, \"60_days_late\": 2.5, \"90_days_late\": 0.5, \"defaults\": 0.0}, \"credit_limit_utilization\": 35.2, \"recent_credit_behavior\": \"stable\"}\n",
"recent_credit_behavior": "stable",
"repayment_behavior": {
"30_days_late": 12.0,
"60_days_late": 2.5,
"90_days_late": 0.5,
"defaults": 0.0,
"never_missed": 85.0
},
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "analyze_credit_history"
},
"analyze_salary_slips": {
"annual_gross": 102000,
"annual_net": 81600,
"deductions": {
"insurance": 300,
"pf": 500,
"tax": 1200
},
"employer": "Tech Corp Ltd",
"employment_duration": "2.5 years",
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.385998,
"end_time": "2025-07-15T11:33:53.363397",
"message_sent": true,
"start_time": "2025-07-15T11:33:52.977399",
"timestamp": "2025-07-15T11:33:53.363397",
"worker_executed": true,
"workers_notified": true
},
"monthly_gross": 8500,
"monthly_net": 6800,
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"recent_increment": true,
"return_code": 0,
"salary_consistency": true,
"status": "completed",
"stderr": "",
"task_id": "analyze_salary_slips"
},
"application_quality_check": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 246,
"model": "gpt-4o-mini",
"output_length": 1204,
"provider": "openai",
"system_message": "You are an expert loan underwriting analyst. Review the application data and assess its quality.\n\nEvaluate:\n1. Data completeness and accuracy\n2. Consistency of information\n3. Red flags or inconsistencies\n4. Overall application quality score (1-100)\n\nReturn a JSON response with your assessment.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or_inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 5.803195,
"end_time": "2025-07-15T11:33:52.005613",
"message_sent": true,
"start_time": "2025-07-15T11:33:46.202418",
"timestamp": "2025-07-15T11:33:52.005613",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or_inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "application_quality_check"
},
"assess_existing_loans": {
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"total_outstanding\": 125000, \"number_of_loans\": 2, \"loan_details\": [{\"type\": \"personal_loan\", \"outstanding\": 45000, \"emi\": 3500, \"remaining_tenure\": \"18 months\"}, {\"type\": \"auto_loan\", \"outstanding\": 80000, \"emi\": 4200, \"remaining_tenure\": \"24 months\"}], \"total_monthly_emi\": 7700, \"repayment_track_record\": \"excellent\", \"loan_burden_ratio\": 0.28}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.310503,
"end_time": "2025-07-15T11:34:01.046498",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.735995",
"timestamp": "2025-07-15T11:34:01.046498",
"worker_executed": true,
"workers_notified": true
},
"loan_burden_ratio": 0.28,
"loan_details": [
{
"emi": 3500,
"outstanding": 45000,
"remaining_tenure": "18 months",
"type": "personal_loan"
},
{
"emi": 4200,
"outstanding": 80000,
"remaining_tenure": "24 months",
"type": "auto_loan"
}
],
"number_of_loans": 2,
"output": "__OUTPUTS__ {\"total_outstanding\": 125000, \"number_of_loans\": 2, \"loan_details\": [{\"type\": \"personal_loan\", \"outstanding\": 45000, \"emi\": 3500, \"remaining_tenure\": \"18 months\"}, {\"type\": \"auto_loan\", \"outstanding\": 80000, \"emi\": 4200, \"remaining_tenure\": \"24 months\"}], \"total_monthly_emi\": 7700, \"repayment_track_record\": \"excellent\", \"loan_burden_ratio\": 0.28}\n",
"repayment_track_record": "excellent",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "assess_existing_loans",
"total_monthly_emi": 7700,
"total_outstanding": 125000
},
"calculate_credit_score": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmppm_nlfej.py\", line 12, in \u003cmodule\u003e\n loan_burden = UNRESOLVED_assess_existing_loans.loan_burden_ratio\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_assess_existing_loans\u0027 is not defined\n",
"execution_details": {
"failed": true,
"message_sent": true,
"worker_executed": true,
"workers_notified": true
},
"output": "Task failed: Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmppm_nlfej.py\", line 12, in \u003cmodule\u003e\n loan_burden = UNRESOLVED_assess_existing_loans.loan_burden_ratio\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_assess_existing_loans\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_credit_score"
},
"calculate_debt_to_income": {
"debt_to_income_ratio": 0.3590686274509804,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"monthly_net_income\": 6800, \"existing_debt_payments\": 2400, \"proposed_emi\": 41.666666666666664, \"total_debt_payments\": 2441.6666666666665, \"debt_to_income_ratio\": 0.3590686274509804, \"ratio_acceptable\": true, \"max_allowed_ratio\": 0.4}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.351119,
"end_time": "2025-07-15T11:33:54.071220",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.720101",
"timestamp": "2025-07-15T11:33:54.071220",
"worker_executed": true,
"workers_notified": true
},
"existing_debt_payments": 2400,
"max_allowed_ratio": 0.4,
"monthly_net_income": 6800,
"output": "__OUTPUTS__ {\"monthly_net_income\": 6800, \"existing_debt_payments\": 2400, \"proposed_emi\": 41.666666666666664, \"total_debt_payments\": 2441.6666666666665, \"debt_to_income_ratio\": 0.3590686274509804, \"ratio_acceptable\": true, \"max_allowed_ratio\": 0.4}\n",
"proposed_emi": 41.666666666666664,
"ratio_acceptable": true,
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "calculate_debt_to_income",
"total_debt_payments": 2441.6666666666665
},
"check_cibil_score": {
"active_accounts": 6,
"closed_accounts": 2,
"credit_history_length": "5 years",
"credit_score": 720,
"credit_utilization": 35.5,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"credit_score\": 720, \"score_range\": \"Good\", \"last_updated\": \"2024-06-01\", \"credit_history_length\": \"5 years\", \"total_accounts\": 8, \"active_accounts\": 6, \"closed_accounts\": 2, \"credit_utilization\": 35.5, \"payment_history\": \"99% on-time\", \"recent_inquiries\": 2}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.310787,
"end_time": "2025-07-15T11:34:00.508520",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.197733",
"timestamp": "2025-07-15T11:34:00.508520",
"worker_executed": true,
"workers_notified": true
},
"last_updated": "2024-06-01",
"output": "__OUTPUTS__ {\"credit_score\": 720, \"score_range\": \"Good\", \"last_updated\": \"2024-06-01\", \"credit_history_length\": \"5 years\", \"total_accounts\": 8, \"active_accounts\": 6, \"closed_accounts\": 2, \"credit_utilization\": 35.5, \"payment_history\": \"99% on-time\", \"recent_inquiries\": 2}\n",
"payment_history": "99% on-time",
"recent_inquiries": 2,
"return_code": 0,
"score_range": "Good",
"status": "completed",
"stderr": "",
"task_id": "check_cibil_score",
"total_accounts": 8
},
"compliance_consolidation": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 571,
"model": "gpt-4o-mini",
"output_length": 1411,
"provider": "openai",
"system_message": "You are a compliance officer specializing in loan underwriting compliance.\n\nReview all compliance check results and provide:\n1. Overall compliance status\n2. Risk assessment\n3. Any compliance gaps or concerns\n4. Recommendations for proceeding\n5. Compliance confidence score (1-100)\n\nReturn a comprehensive compliance assessment in JSON format.\n"
},
"input_format": "json",
"model_client_id": "compliance_reviewer",
"output": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"high_risk_areas\\\": [\\n \\\"KYC Compliance\\\",\\n \\\"AML Screening\\\",\\n \\\"Regulatory Compliance\\\"\\n ],\\n \\\"low_risk_areas\\\": [\\n \\\"Internal Policy\\\"\\n ]\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n {\\n \\\"issue\\\": \\\"KYC Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"AML Screening\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"Regulatory Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Non-compliance with regulatory requirements can result in fines and damage to reputation.\\\"\\n }\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\\\",\\n \\\"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\\\",\\n \\\"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\\\",\\n \\\"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 30\\n}\\n```\", \"model_client_id\": \"compliance_reviewer\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 7.875902,
"end_time": "2025-07-15T11:37:23.790671",
"message_sent": true,
"start_time": "2025-07-15T11:37:15.914769",
"timestamp": "2025-07-15T11:37:23.790671",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "compliance_reviewer",
"output": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"high_risk_areas\\\": [\\n \\\"KYC Compliance\\\",\\n \\\"AML Screening\\\",\\n \\\"Regulatory Compliance\\\"\\n ],\\n \\\"low_risk_areas\\\": [\\n \\\"Internal Policy\\\"\\n ]\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n {\\n \\\"issue\\\": \\\"KYC Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"AML Screening\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"Regulatory Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Non-compliance with regulatory requirements can result in fines and damage to reputation.\\\"\\n }\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\\\",\\n \\\"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\\\",\\n \\\"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\\\",\\n \\\"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 30\\n}\\n```\", \"model_client_id\": \"compliance_reviewer\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "compliance_consolidation"
},
"consolidate_document_verification": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 822,
"model": "gpt-4o-mini",
"output_length": 336,
"provider": "openai",
"system_message": "You are a document verification specialist. Analyze all document verification results and provide a comprehensive assessment.\n\nConsider:\n1. Overall verification success rate\n2. Consistency across documents\n3. Risk factors identified\n4. Recommendation for proceeding\n\nReturn a JSON response with your analysis.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_verification_success_rate\\\": 100,\\n \\\"consistency_across_documents\\\": {\\n \\\"name_match\\\": true,\\n \\\"address_match\\\": true\\n },\\n \\\"risk_factors_identified\\\": [],\\n \\\"recommendation_for_proceeding\\\": \\\"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 2.601936,
"end_time": "2025-07-15T11:33:53.779011",
"message_sent": true,
"start_time": "2025-07-15T11:33:51.177075",
"timestamp": "2025-07-15T11:33:53.779011",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_verification_success_rate\\\": 100,\\n \\\"consistency_across_documents\\\": {\\n \\\"name_match\\\": true,\\n \\\"address_match\\\": true\\n },\\n \\\"risk_factors_identified\\\": [],\\n \\\"recommendation_for_proceeding\\\": \\\"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "consolidate_document_verification"
},
"evaluate_default_risk": {
"agent_type": "assistant",
"ai_response": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
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"model": "gpt-4o-mini",
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"provider": "openai",
"system_message": "You are a credit risk assessment specialist. Analyze the provided credit data and calculate default risk.\n\nConsider:\n1. Credit score and history\n2. Repayment patterns\n3. Credit utilization\n4. Existing loan burden\n5. Overall risk profile\n\nProvide a risk score (1-100, where 100 is highest risk) and detailed risk analysis.\n"
},
"input_format": "json",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score.\\\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\\n- **Credit History**: The credit history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\\n\\n### 4. Existing Loan Burden\\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\\n\\n### 5. Overall Risk Profile\\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\\n\\n### Risk Score Calculation\\nBased on the analysis:\\n- CIBIL Score: Unknown (assumed neutral)\\n- Credit History: Excellent (low risk)\\n- Repayment Patterns: Excellent (low risk)\\n- Credit Utilization: 28% (moderate risk)\\n- Existing Loan Burden: Manageable (low risk)\\n\\n**Risk Score Calculation**:\\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\\n- Credit History: 10 (excellent)\\n- Repayment Patterns: 10 (excellent)\\n- Credit Utilization: 30 (moderate)\\n- Existing Loan Burden: 20 (manageable)\\n\\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\\n\\n### Final Risk Score\\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\\n\\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 14.140537,
"end_time": "2025-07-15T11:34:15.138994",
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"start_time": "2025-07-15T11:34:00.998457",
"timestamp": "2025-07-15T11:34:15.138994",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score.\\\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\\n- **Credit History**: The credit history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\\n\\n### 4. Existing Loan Burden\\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\\n\\n### 5. Overall Risk Profile\\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\\n\\n### Risk Score Calculation\\nBased on the analysis:\\n- CIBIL Score: Unknown (assumed neutral)\\n- Credit History: Excellent (low risk)\\n- Repayment Patterns: Excellent (low risk)\\n- Credit Utilization: 28% (moderate risk)\\n- Existing Loan Burden: Manageable (low risk)\\n\\n**Risk Score Calculation**:\\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\\n- Credit History: 10 (excellent)\\n- Repayment Patterns: 10 (excellent)\\n- Credit Utilization: 30 (moderate)\\n- Existing Loan Burden: 20 (manageable)\\n\\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\\n\\n### Final Risk Score\\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\\n\\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
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"task_id": "evaluate_default_risk"
},
"execution_summary": {
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},
"extract_application_info": {
"customer_info": {
"address": "123 Main St, City, State 12345",
"annual_income": 75000,
"customer_id": "CUST_12345",
"email": "john.doe@email.com",
"employment_status": "employed",
"full_name": "John Doe",
"loan_purpose": "home_purchase",
"phone": "+1-555-0123",
"requested_amount": 10000
},
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"extraction_success\": true, \"customer_info\": {\"customer_id\": \"CUST_12345\", \"full_name\": \"John Doe\", \"email\": \"john.doe@email.com\", \"phone\": \"+1-555-0123\", \"address\": \"123 Main St, City, State 12345\", \"employment_status\": \"employed\", \"annual_income\": 75000, \"loan_purpose\": \"home_purchase\", \"requested_amount\": 10000}, \"missing_fields\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.251678,
"end_time": "2025-07-15T11:33:45.987539",
"message_sent": true,
"start_time": "2025-07-15T11:33:45.735861",
"timestamp": "2025-07-15T11:33:45.987539",
"worker_executed": true,
"workers_notified": true
},
"extraction_success": true,
"missing_fields": [],
"output": "__OUTPUTS__ {\"extraction_success\": true, \"customer_info\": {\"customer_id\": \"CUST_12345\", \"full_name\": \"John Doe\", \"email\": \"john.doe@email.com\", \"phone\": \"+1-555-0123\", \"address\": \"123 Main St, City, State 12345\", \"employment_status\": \"employed\", \"annual_income\": 75000, \"loan_purpose\": \"home_purchase\", \"requested_amount\": 10000}, \"missing_fields\": []}\n",
"return_code": 0,
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"stderr": "",
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},
"final_underwriting_analysis": {
"agent_type": "assistant",
"ai_response": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.",
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"model": "gpt-4o-mini",
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"provider": "openai",
"system_message": "You are a senior underwriting manager making final loan decisions.\n\nAnalyze all assessment results and provide:\n1. Final recommendation (APPROVE/CONDITIONAL/DECLINE)\n2. Loan terms and conditions\n3. Risk mitigation measures\n4. Reasoning for decision\n5. Confidence level (1-100)\n\nConsider all factors: income, credit, compliance, and overall risk profile.\n"
},
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"output": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation**: DECLINE\\n\\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\\n\\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\\n\\n4. **Reasoning for Decision**:\\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \\n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\\n\\n5. **Confidence Level**: 95\\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"output": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation**: DECLINE\\n\\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\\n\\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\\n\\n4. **Reasoning for Decision**:\\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \\n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\\n\\n5. **Confidence Level**: 95\\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"generate_final_output": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmpnup8ld_2.py\", line 15, in \u003cmodule\u003e\n \"credit_score\": UNRESOLVED_calculate_credit_score.internal_credit_score,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_calculate_credit_score\u0027 is not defined\n",
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"agent_type": "assistant",
"ai_response": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```",
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"system_message": "You are a senior financial analyst specializing in income assessment for loan underwriting.\n\nAnalyze the provided income data and provide:\n1. Income stability assessment\n2. Debt servicing capability\n3. Financial discipline evaluation\n4. Risk factors and recommendations\n5. Overall income adequacy score (1-100)\n\nReturn a detailed JSON assessment.\n"
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"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"Stable\\\",\\n \\\"details\\\": {\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"probation_status\\\": \\\"Confirmed\\\",\\n \\\"employment_verified\\\": true,\\n \\\"reference_check_passed\\\": true\\n }\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Adequate\\\",\\n \\\"details\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true\\n }\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Good\\\",\\n \\\"details\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"monthly_net_income\\\": 6800,\\n \\\"savings_rate\\\": \\\"Not provided, but implied stability suggests good financial habits.\\\"\\n }\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low\\\",\\n \\\"details\\\": {\\n \\\"employment stability\\\": \\\"High due to permanent position and strong employer rating.\\\",\\n \\\"debt levels\\\": \\\"Moderate, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"external factors\\\": \\\"No significant external risk factors identified.\\\"\\n }\\n },\\n \\\"recommendations\\\": {\\n \\\"assessment\\\": \\\"Maintain current financial practices.\\\",\\n \\\"details\\\": {\\n \\\"suggested_actions\\\": [\\n \\\"Continue to monitor and manage existing debt levels.\\\",\\n \\\"Consider increasing savings to further improve financial resilience.\\\",\\n \\\"Maintain employment stability through continuous skill development.\\\"\\n ]\\n }\\n },\\n \\\"overall_income_adequacy_score\\\": 85\\n }\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"Stable\\\",\\n \\\"details\\\": {\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"probation_status\\\": \\\"Confirmed\\\",\\n \\\"employment_verified\\\": true,\\n \\\"reference_check_passed\\\": true\\n }\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Adequate\\\",\\n \\\"details\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true\\n }\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Good\\\",\\n \\\"details\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"monthly_net_income\\\": 6800,\\n \\\"savings_rate\\\": \\\"Not provided, but implied stability suggests good financial habits.\\\"\\n }\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low\\\",\\n \\\"details\\\": {\\n \\\"employment stability\\\": \\\"High due to permanent position and strong employer rating.\\\",\\n \\\"debt levels\\\": \\\"Moderate, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"external factors\\\": \\\"No significant external risk factors identified.\\\"\\n }\\n },\\n \\\"recommendations\\\": {\\n \\\"assessment\\\": \\\"Maintain current financial practices.\\\",\\n \\\"details\\\": {\\n \\\"suggested_actions\\\": [\\n \\\"Continue to monitor and manage existing debt levels.\\\",\\n \\\"Consider increasing savings to further improve financial resilience.\\\",\\n \\\"Maintain employment stability through continuous skill development.\\\"\\n ]\\n }\\n },\\n \\\"overall_income_adequacy_score\\\": 85\\n }\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"output": "__OUTPUTS__ {\"oldest_account_age\": \"60 months\", \"average_account_age\": \"32 months\", \"credit_mix\": {\"credit_cards\": 3, \"personal_loans\": 1, \"auto_loans\": 1, \"home_loans\": 0, \"other\": 1}, \"repayment_behavior\": {\"never_missed\": 85.0, \"30_days_late\": 12.0, \"60_days_late\": 2.5, \"90_days_late\": 0.5, \"defaults\": 0.0}, \"credit_limit_utilization\": 35.2, \"recent_credit_behavior\": \"stable\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.336491,
"end_time": "2025-07-15T11:34:00.792280",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.455789",
"timestamp": "2025-07-15T11:34:00.792280",
"worker_executed": true,
"workers_notified": true
},
"oldest_account_age": "60 months",
"output": "__OUTPUTS__ {\"oldest_account_age\": \"60 months\", \"average_account_age\": \"32 months\", \"credit_mix\": {\"credit_cards\": 3, \"personal_loans\": 1, \"auto_loans\": 1, \"home_loans\": 0, \"other\": 1}, \"repayment_behavior\": {\"never_missed\": 85.0, \"30_days_late\": 12.0, \"60_days_late\": 2.5, \"90_days_late\": 0.5, \"defaults\": 0.0}, \"credit_limit_utilization\": 35.2, \"recent_credit_behavior\": \"stable\"}\n",
"recent_credit_behavior": "stable",
"repayment_behavior": {
"30_days_late": 12.0,
"60_days_late": 2.5,
"90_days_late": 0.5,
"defaults": 0.0,
"never_missed": 85.0
},
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "analyze_credit_history"
},
"analyze_salary_slips": {
"annual_gross": 102000,
"annual_net": 81600,
"deductions": {
"insurance": 300,
"pf": 500,
"tax": 1200
},
"employer": "Tech Corp Ltd",
"employment_duration": "2.5 years",
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.385998,
"end_time": "2025-07-15T11:33:53.363397",
"message_sent": true,
"start_time": "2025-07-15T11:33:52.977399",
"timestamp": "2025-07-15T11:33:53.363397",
"worker_executed": true,
"workers_notified": true
},
"monthly_gross": 8500,
"monthly_net": 6800,
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"recent_increment": true,
"return_code": 0,
"salary_consistency": true,
"status": "completed",
"stderr": "",
"task_id": "analyze_salary_slips"
},
"application_quality_check": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 246,
"model": "gpt-4o-mini",
"output_length": 1204,
"provider": "openai",
"system_message": "You are an expert loan underwriting analyst. Review the application data and assess its quality.\n\nEvaluate:\n1. Data completeness and accuracy\n2. Consistency of information\n3. Red flags or inconsistencies\n4. Overall application quality score (1-100)\n\nReturn a JSON response with your assessment.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or_inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 5.803195,
"end_time": "2025-07-15T11:33:52.005613",
"message_sent": true,
"start_time": "2025-07-15T11:33:46.202418",
"timestamp": "2025-07-15T11:33:52.005613",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or_inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or_inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. However, further analysis of the actual customer info would be necessary to identify any potential issues.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. The absence of missing fields and successful validation contributes positively to the overall score. A score of 95 reflects a strong application, though further details on customer info would enhance the assessment.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "application_quality_check"
},
"assess_existing_loans": {
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"total_outstanding\": 125000, \"number_of_loans\": 2, \"loan_details\": [{\"type\": \"personal_loan\", \"outstanding\": 45000, \"emi\": 3500, \"remaining_tenure\": \"18 months\"}, {\"type\": \"auto_loan\", \"outstanding\": 80000, \"emi\": 4200, \"remaining_tenure\": \"24 months\"}], \"total_monthly_emi\": 7700, \"repayment_track_record\": \"excellent\", \"loan_burden_ratio\": 0.28}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.310503,
"end_time": "2025-07-15T11:34:01.046498",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.735995",
"timestamp": "2025-07-15T11:34:01.046498",
"worker_executed": true,
"workers_notified": true
},
"loan_burden_ratio": 0.28,
"loan_details": [
{
"emi": 3500,
"outstanding": 45000,
"remaining_tenure": "18 months",
"type": "personal_loan"
},
{
"emi": 4200,
"outstanding": 80000,
"remaining_tenure": "24 months",
"type": "auto_loan"
}
],
"number_of_loans": 2,
"output": "__OUTPUTS__ {\"total_outstanding\": 125000, \"number_of_loans\": 2, \"loan_details\": [{\"type\": \"personal_loan\", \"outstanding\": 45000, \"emi\": 3500, \"remaining_tenure\": \"18 months\"}, {\"type\": \"auto_loan\", \"outstanding\": 80000, \"emi\": 4200, \"remaining_tenure\": \"24 months\"}], \"total_monthly_emi\": 7700, \"repayment_track_record\": \"excellent\", \"loan_burden_ratio\": 0.28}\n",
"repayment_track_record": "excellent",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "assess_existing_loans",
"total_monthly_emi": 7700,
"total_outstanding": 125000
},
"calculate_credit_score": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmppm_nlfej.py\", line 12, in \u003cmodule\u003e\n loan_burden = UNRESOLVED_assess_existing_loans.loan_burden_ratio\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_assess_existing_loans\u0027 is not defined\n",
"execution_details": {
"failed": true,
"message_sent": true,
"worker_executed": true,
"workers_notified": true
},
"output": "Task failed: Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmppm_nlfej.py\", line 12, in \u003cmodule\u003e\n loan_burden = UNRESOLVED_assess_existing_loans.loan_burden_ratio\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_assess_existing_loans\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_credit_score"
},
"calculate_debt_to_income": {
"debt_to_income_ratio": 0.3590686274509804,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"monthly_net_income\": 6800, \"existing_debt_payments\": 2400, \"proposed_emi\": 41.666666666666664, \"total_debt_payments\": 2441.6666666666665, \"debt_to_income_ratio\": 0.3590686274509804, \"ratio_acceptable\": true, \"max_allowed_ratio\": 0.4}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.351119,
"end_time": "2025-07-15T11:33:54.071220",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.720101",
"timestamp": "2025-07-15T11:33:54.071220",
"worker_executed": true,
"workers_notified": true
},
"existing_debt_payments": 2400,
"max_allowed_ratio": 0.4,
"monthly_net_income": 6800,
"output": "__OUTPUTS__ {\"monthly_net_income\": 6800, \"existing_debt_payments\": 2400, \"proposed_emi\": 41.666666666666664, \"total_debt_payments\": 2441.6666666666665, \"debt_to_income_ratio\": 0.3590686274509804, \"ratio_acceptable\": true, \"max_allowed_ratio\": 0.4}\n",
"proposed_emi": 41.666666666666664,
"ratio_acceptable": true,
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "calculate_debt_to_income",
"total_debt_payments": 2441.6666666666665
},
"check_cibil_score": {
"active_accounts": 6,
"closed_accounts": 2,
"credit_history_length": "5 years",
"credit_score": 720,
"credit_utilization": 35.5,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"credit_score\": 720, \"score_range\": \"Good\", \"last_updated\": \"2024-06-01\", \"credit_history_length\": \"5 years\", \"total_accounts\": 8, \"active_accounts\": 6, \"closed_accounts\": 2, \"credit_utilization\": 35.5, \"payment_history\": \"99% on-time\", \"recent_inquiries\": 2}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.310787,
"end_time": "2025-07-15T11:34:00.508520",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.197733",
"timestamp": "2025-07-15T11:34:00.508520",
"worker_executed": true,
"workers_notified": true
},
"last_updated": "2024-06-01",
"output": "__OUTPUTS__ {\"credit_score\": 720, \"score_range\": \"Good\", \"last_updated\": \"2024-06-01\", \"credit_history_length\": \"5 years\", \"total_accounts\": 8, \"active_accounts\": 6, \"closed_accounts\": 2, \"credit_utilization\": 35.5, \"payment_history\": \"99% on-time\", \"recent_inquiries\": 2}\n",
"payment_history": "99% on-time",
"recent_inquiries": 2,
"return_code": 0,
"score_range": "Good",
"status": "completed",
"stderr": "",
"task_id": "check_cibil_score",
"total_accounts": 8
},
"compliance_consolidation": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 571,
"model": "gpt-4o-mini",
"output_length": 1411,
"provider": "openai",
"system_message": "You are a compliance officer specializing in loan underwriting compliance.\n\nReview all compliance check results and provide:\n1. Overall compliance status\n2. Risk assessment\n3. Any compliance gaps or concerns\n4. Recommendations for proceeding\n5. Compliance confidence score (1-100)\n\nReturn a comprehensive compliance assessment in JSON format.\n"
},
"input_format": "json",
"model_client_id": "compliance_reviewer",
"output": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"high_risk_areas\\\": [\\n \\\"KYC Compliance\\\",\\n \\\"AML Screening\\\",\\n \\\"Regulatory Compliance\\\"\\n ],\\n \\\"low_risk_areas\\\": [\\n \\\"Internal Policy\\\"\\n ]\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n {\\n \\\"issue\\\": \\\"KYC Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"AML Screening\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"Regulatory Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Non-compliance with regulatory requirements can result in fines and damage to reputation.\\\"\\n }\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\\\",\\n \\\"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\\\",\\n \\\"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\\\",\\n \\\"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 30\\n}\\n```\", \"model_client_id\": \"compliance_reviewer\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 7.875902,
"end_time": "2025-07-15T11:37:23.790671",
"message_sent": true,
"start_time": "2025-07-15T11:37:15.914769",
"timestamp": "2025-07-15T11:37:23.790671",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "compliance_reviewer",
"output": "```json\n{\n \"overall_compliance_status\": \"Non-compliant\",\n \"risk_assessment\": {\n \"high_risk_areas\": [\n \"KYC Compliance\",\n \"AML Screening\",\n \"Regulatory Compliance\"\n ],\n \"low_risk_areas\": [\n \"Internal Policy\"\n ]\n },\n \"compliance_gaps_or_concerns\": [\n {\n \"issue\": \"KYC Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\"\n },\n {\n \"issue\": \"AML Screening\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\"\n },\n {\n \"issue\": \"Regulatory Compliance\",\n \"status\": \"UNRESOLVED\",\n \"concern\": \"Non-compliance with regulatory requirements can result in fines and damage to reputation.\"\n }\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\",\n \"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\",\n \"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\",\n \"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\"\n ],\n \"compliance_confidence_score\": 30\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"high_risk_areas\\\": [\\n \\\"KYC Compliance\\\",\\n \\\"AML Screening\\\",\\n \\\"Regulatory Compliance\\\"\\n ],\\n \\\"low_risk_areas\\\": [\\n \\\"Internal Policy\\\"\\n ]\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n {\\n \\\"issue\\\": \\\"KYC Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Failure to meet Know Your Customer requirements may lead to identity fraud and regulatory penalties.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"AML Screening\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Inadequate Anti-Money Laundering screening could expose the organization to financial crimes and legal repercussions.\\\"\\n },\\n {\\n \\\"issue\\\": \\\"Regulatory Compliance\\\",\\n \\\"status\\\": \\\"UNRESOLVED\\\",\\n \\\"concern\\\": \\\"Non-compliance with regulatory requirements can result in fines and damage to reputation.\\\"\\n }\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address the unresolved KYC compliance issues by implementing a robust verification process.\\\",\\n \\\"Enhance AML screening procedures to ensure all transactions are monitored and flagged appropriately.\\\",\\n \\\"Conduct a thorough review of regulatory compliance requirements and rectify any outstanding issues.\\\",\\n \\\"Continue to maintain and monitor internal policy compliance, as it currently meets the necessary criteria.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 30\\n}\\n```\", \"model_client_id\": \"compliance_reviewer\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "compliance_consolidation"
},
"consolidate_document_verification": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 822,
"model": "gpt-4o-mini",
"output_length": 336,
"provider": "openai",
"system_message": "You are a document verification specialist. Analyze all document verification results and provide a comprehensive assessment.\n\nConsider:\n1. Overall verification success rate\n2. Consistency across documents\n3. Risk factors identified\n4. Recommendation for proceeding\n\nReturn a JSON response with your analysis.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_verification_success_rate\\\": 100,\\n \\\"consistency_across_documents\\\": {\\n \\\"name_match\\\": true,\\n \\\"address_match\\\": true\\n },\\n \\\"risk_factors_identified\\\": [],\\n \\\"recommendation_for_proceeding\\\": \\\"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 2.601936,
"end_time": "2025-07-15T11:33:53.779011",
"message_sent": true,
"start_time": "2025-07-15T11:33:51.177075",
"timestamp": "2025-07-15T11:33:53.779011",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"overall_verification_success_rate\": 100,\n \"consistency_across_documents\": {\n \"name_match\": true,\n \"address_match\": true\n },\n \"risk_factors_identified\": [],\n \"recommendation_for_proceeding\": \"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_verification_success_rate\\\": 100,\\n \\\"consistency_across_documents\\\": {\\n \\\"name_match\\\": true,\\n \\\"address_match\\\": true\\n },\\n \\\"risk_factors_identified\\\": [],\\n \\\"recommendation_for_proceeding\\\": \\\"All documents have been successfully verified with high scores and consistent information. Proceed with confidence.\\\"\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "consolidate_document_verification"
},
"evaluate_default_risk": {
"agent_type": "assistant",
"ai_response": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 561,
"model": "gpt-4o-mini",
"output_length": 3456,
"provider": "openai",
"system_message": "You are a credit risk assessment specialist. Analyze the provided credit data and calculate default risk.\n\nConsider:\n1. Credit score and history\n2. Repayment patterns\n3. Credit utilization\n4. Existing loan burden\n5. Overall risk profile\n\nProvide a risk score (1-100, where 100 is highest risk) and detailed risk analysis.\n"
},
"input_format": "json",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score.\\\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\\n- **Credit History**: The credit history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\\n\\n### 4. Existing Loan Burden\\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\\n\\n### 5. Overall Risk Profile\\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\\n\\n### Risk Score Calculation\\nBased on the analysis:\\n- CIBIL Score: Unknown (assumed neutral)\\n- Credit History: Excellent (low risk)\\n- Repayment Patterns: Excellent (low risk)\\n- Credit Utilization: 28% (moderate risk)\\n- Existing Loan Burden: Manageable (low risk)\\n\\n**Risk Score Calculation**:\\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\\n- Credit History: 10 (excellent)\\n- Repayment Patterns: 10 (excellent)\\n- Credit Utilization: 30 (moderate)\\n- Existing Loan Burden: 20 (manageable)\\n\\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\\n\\n### Final Risk Score\\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\\n\\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 14.140537,
"end_time": "2025-07-15T11:34:15.138994",
"message_sent": true,
"start_time": "2025-07-15T11:34:00.998457",
"timestamp": "2025-07-15T11:34:15.138994",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score.\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\n- **Credit History**: The credit history is also marked as \"UNRESOLVED_analyze_credit_history.\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\n\n### 4. Existing Loan Burden\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\n\n### 5. Overall Risk Profile\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\n\n### Risk Score Calculation\nBased on the analysis:\n- CIBIL Score: Unknown (assumed neutral)\n- Credit History: Excellent (low risk)\n- Repayment Patterns: Excellent (low risk)\n- Credit Utilization: 28% (moderate risk)\n- Existing Loan Burden: Manageable (low risk)\n\n**Risk Score Calculation**:\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\n- Credit History: 10 (excellent)\n- Repayment Patterns: 10 (excellent)\n- Credit Utilization: 30 (moderate)\n- Existing Loan Burden: 20 (manageable)\n\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\n\n### Final Risk Score\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\n\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data for default risk, we will evaluate the various factors mentioned and assign a risk score based on the overall assessment.\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score.\\\" Without a specific score, we cannot assess this factor accurately. However, if we assume that the score is below 650, it would indicate a higher risk.\\n- **Credit History**: The credit history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" If the history shows late payments or defaults, it would increase the risk. Assuming a good credit history based on the repayment track record, we can tentatively consider this factor as neutral to positive.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the individual has consistently made payments on time, which is a positive sign and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%). This is a reasonable level of debt relative to income, suggesting that the individual is not over-leveraged. A lower ratio is generally favorable, indicating a lower risk of default.\\n\\n### 4. Existing Loan Burden\\n- **Total Monthly EMI**: The total monthly EMI is 7700. This is a manageable amount, especially if the individual\u0027s income supports this level of repayment.\\n- **Outstanding Loans**: The total outstanding amount is 125,000, which includes a personal loan of 45,000 and an auto loan of 80,000. The distribution of loans appears balanced, and the remaining tenures are reasonable.\\n\\n### 5. Overall Risk Profile\\n- **Number of Loans**: The individual has two loans, which is manageable. However, having multiple loans can increase risk if the individual faces financial difficulties.\\n- **Total Outstanding**: The total outstanding amount is significant but manageable given the repayment track record and loan burden ratio.\\n\\n### Risk Score Calculation\\nBased on the analysis:\\n- CIBIL Score: Unknown (assumed neutral)\\n- Credit History: Excellent (low risk)\\n- Repayment Patterns: Excellent (low risk)\\n- Credit Utilization: 28% (moderate risk)\\n- Existing Loan Burden: Manageable (low risk)\\n\\n**Risk Score Calculation**:\\n- CIBIL Score: 50 (assumed average risk due to unresolved status)\\n- Credit History: 10 (excellent)\\n- Repayment Patterns: 10 (excellent)\\n- Credit Utilization: 30 (moderate)\\n- Existing Loan Burden: 20 (manageable)\\n\\n**Total Risk Score** = (50 + 10 + 10 + 30 + 20) / 5 = 24\\n\\n### Final Risk Score\\n**Risk Score**: 24 (on a scale of 1-100, where 100 is highest risk)\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to understand the exact credit standing.\\n2. **Maintain Excellent Repayment Patterns**: Continue to make timely payments to preserve the excellent repayment track record.\\n3. **Monitor Credit Utilization**: Keep the loan burden ratio below 30% to maintain a healthy credit profile.\\n4. **Consider Financial Planning**: If planning to take on additional loans, ensure that the total EMI remains manageable relative to income.\\n5. **Regularly Review Credit Reports**: Regularly check credit reports for any discrepancies and resolve them promptly to maintain a good credit history.\\n\\nOverall, the individual appears to have a low risk of default based on the available data, but resolving the CIBIL score and credit history will provide a clearer picture.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "evaluate_default_risk"
},
"extract_application_info": {
"customer_info": {
"address": "123 Main St, City, State 12345",
"annual_income": 75000,
"customer_id": "CUST_12345",
"email": "john.doe@email.com",
"employment_status": "employed",
"full_name": "John Doe",
"loan_purpose": "home_purchase",
"phone": "+1-555-0123",
"requested_amount": 10000
},
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"extraction_success\": true, \"customer_info\": {\"customer_id\": \"CUST_12345\", \"full_name\": \"John Doe\", \"email\": \"john.doe@email.com\", \"phone\": \"+1-555-0123\", \"address\": \"123 Main St, City, State 12345\", \"employment_status\": \"employed\", \"annual_income\": 75000, \"loan_purpose\": \"home_purchase\", \"requested_amount\": 10000}, \"missing_fields\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.251678,
"end_time": "2025-07-15T11:33:45.987539",
"message_sent": true,
"start_time": "2025-07-15T11:33:45.735861",
"timestamp": "2025-07-15T11:33:45.987539",
"worker_executed": true,
"workers_notified": true
},
"extraction_success": true,
"missing_fields": [],
"output": "__OUTPUTS__ {\"extraction_success\": true, \"customer_info\": {\"customer_id\": \"CUST_12345\", \"full_name\": \"John Doe\", \"email\": \"john.doe@email.com\", \"phone\": \"+1-555-0123\", \"address\": \"123 Main St, City, State 12345\", \"employment_status\": \"employed\", \"annual_income\": 75000, \"loan_purpose\": \"home_purchase\", \"requested_amount\": 10000}, \"missing_fields\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "extract_application_info"
},
"final_underwriting_analysis": {
"agent_type": "assistant",
"ai_response": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.",
"execution_details": {
"actual_result": {
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"agent_type": "assistant",
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"model": "gpt-4o-mini",
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"provider": "openai",
"system_message": "You are a senior underwriting manager making final loan decisions.\n\nAnalyze all assessment results and provide:\n1. Final recommendation (APPROVE/CONDITIONAL/DECLINE)\n2. Loan terms and conditions\n3. Risk mitigation measures\n4. Reasoning for decision\n5. Confidence level (1-100)\n\nConsider all factors: income, credit, compliance, and overall risk profile.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation**: DECLINE\\n\\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\\n\\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\\n\\n4. **Reasoning for Decision**:\\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \\n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\\n\\n5. **Confidence Level**: 95\\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 4.938984,
"end_time": "2025-07-15T11:37:26.673292",
"message_sent": true,
"start_time": "2025-07-15T11:37:21.734308",
"timestamp": "2025-07-15T11:37:26.673292",
"worker_executed": true,
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},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "1. **Final Recommendation**: DECLINE\n\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\n\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\n\n4. **Reasoning for Decision**:\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\n\n5. **Confidence Level**: 95\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation**: DECLINE\\n\\n2. **Loan Terms and Conditions**: N/A (Loan application is declined)\\n\\n3. **Risk Mitigation Measures**: N/A (Loan application is declined)\\n\\n4. **Reasoning for Decision**:\\n - The application has multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculation. \\n - Each of these unresolved factors poses a significant risk to the underwriting process. Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to accurately assess the applicant\u0027s ability to repay the loan.\\n - The unresolved compliance issues further complicate the situation, as they may indicate potential legal or regulatory risks associated with the loan.\\n - Given the lack of clarity and the potential for high risk, it is prudent to decline the application at this stage.\\n\\n5. **Confidence Level**: 95\\n - The high confidence level reflects the clear risks associated with the unresolved issues. The decision to decline is based on a thorough understanding of underwriting principles and the necessity for complete and verified information to make informed lending decisions.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"generate_final_output": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmpnup8ld_2.py\", line 15, in \u003cmodule\u003e\n \"credit_score\": UNRESOLVED_calculate_credit_score.internal_credit_score,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_calculate_credit_score\u0027 is not defined\n",
"execution_details": {
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},
"output": "Task failed: Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmpnup8ld_2.py\", line 15, in \u003cmodule\u003e\n \"credit_score\": UNRESOLVED_calculate_credit_score.internal_credit_score,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_calculate_credit_score\u0027 is not defined\n",
"status": "FAILED",
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"income_assessment": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```",
"execution_details": {
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"agent_type": "assistant",
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"agent_type": "assistant",
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"model": "gpt-4o-mini",
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"provider": "openai",
"system_message": "You are a senior financial analyst specializing in income assessment for loan underwriting.\n\nAnalyze the provided income data and provide:\n1. Income stability assessment\n2. Debt servicing capability\n3. Financial discipline evaluation\n4. Risk factors and recommendations\n5. Overall income adequacy score (1-100)\n\nReturn a detailed JSON assessment.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"Stable\\\",\\n \\\"details\\\": {\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"probation_status\\\": \\\"Confirmed\\\",\\n \\\"employment_verified\\\": true,\\n \\\"reference_check_passed\\\": true\\n }\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Adequate\\\",\\n \\\"details\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true\\n }\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Good\\\",\\n \\\"details\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"monthly_net_income\\\": 6800,\\n \\\"savings_rate\\\": \\\"Not provided, but implied stability suggests good financial habits.\\\"\\n }\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low\\\",\\n \\\"details\\\": {\\n \\\"employment stability\\\": \\\"High due to permanent position and strong employer rating.\\\",\\n \\\"debt levels\\\": \\\"Moderate, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"external factors\\\": \\\"No significant external risk factors identified.\\\"\\n }\\n },\\n \\\"recommendations\\\": {\\n \\\"assessment\\\": \\\"Maintain current financial practices.\\\",\\n \\\"details\\\": {\\n \\\"suggested_actions\\\": [\\n \\\"Continue to monitor and manage existing debt levels.\\\",\\n \\\"Consider increasing savings to further improve financial resilience.\\\",\\n \\\"Maintain employment stability through continuous skill development.\\\"\\n ]\\n }\\n },\\n \\\"overall_income_adequacy_score\\\": 85\\n }\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"status": "completed",
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},
"duration_seconds": 7.046686,
"end_time": "2025-07-15T11:34:01.075014",
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"start_time": "2025-07-15T11:33:54.028328",
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"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"Stable\",\n \"details\": {\n \"employment_type\": \"Permanent\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"probation_status\": \"Confirmed\",\n \"employment_verified\": true,\n \"reference_check_passed\": true\n }\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Adequate\",\n \"details\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"proposed_emi\": 41.67,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true\n }\n },\n \"financial_discipline\": {\n \"assessment\": \"Good\",\n \"details\": {\n \"existing_debt_payments\": 2400,\n \"debt_to_income_ratio\": 0.3591,\n \"monthly_net_income\": 6800,\n \"savings_rate\": \"Not provided, but implied stability suggests good financial habits.\"\n }\n },\n \"risk_factors\": {\n \"assessment\": \"Low\",\n \"details\": {\n \"employment stability\": \"High due to permanent position and strong employer rating.\",\n \"debt levels\": \"Moderate, with a debt-to-income ratio below the acceptable threshold.\",\n \"external factors\": \"No significant external risk factors identified.\"\n }\n },\n \"recommendations\": {\n \"assessment\": \"Maintain current financial practices.\",\n \"details\": {\n \"suggested_actions\": [\n \"Continue to monitor and manage existing debt levels.\",\n \"Consider increasing savings to further improve financial resilience.\",\n \"Maintain employment stability through continuous skill development.\"\n ]\n }\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"Stable\\\",\\n \\\"details\\\": {\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"probation_status\\\": \\\"Confirmed\\\",\\n \\\"employment_verified\\\": true,\\n \\\"reference_check_passed\\\": true\\n }\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Adequate\\\",\\n \\\"details\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true\\n }\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Good\\\",\\n \\\"details\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"monthly_net_income\\\": 6800,\\n \\\"savings_rate\\\": \\\"Not provided, but implied stability suggests good financial habits.\\\"\\n }\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low\\\",\\n \\\"details\\\": {\\n \\\"employment stability\\\": \\\"High due to permanent position and strong employer rating.\\\",\\n \\\"debt levels\\\": \\\"Moderate, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"external factors\\\": \\\"No significant external risk factors identified.\\\"\\n }\\n },\\n \\\"recommendations\\\": {\\n \\\"assessment\\\": \\\"Maintain current financial practices.\\\",\\n \\\"details\\\": {\\n \\\"suggested_actions\\\": [\\n \\\"Continue to monitor and manage existing debt levels.\\\",\\n \\\"Consider increasing savings to further improve financial resilience.\\\",\\n \\\"Maintain employment stability through continuous skill development.\\\"\\n ]\\n }\\n },\\n \\\"overall_income_adequacy_score\\\": 85\\n }\\n}\\n```\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
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"output": "__OUTPUTS__ {\"loan_amount_within_limits\": true, \"ltv_ratio_acceptable\": true, \"income_criteria_met\": true, \"age_criteria_met\": true, \"employment_criteria_met\": true, \"credit_score_threshold_met\": true, \"geographic_restrictions_met\": true, \"policy_compliance_score\": 95.5, \"exceptions_required\": [], \"policy_version\": \"2024.1\"}\n",
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},
"geographic_restrictions_met": true,
"income_criteria_met": true,
"loan_amount_within_limits": true,
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"output": "__OUTPUTS__ {\"loan_amount_within_limits\": true, \"ltv_ratio_acceptable\": true, \"income_criteria_met\": true, \"age_criteria_met\": true, \"employment_criteria_met\": true, \"credit_score_threshold_met\": true, \"geographic_restrictions_met\": true, \"policy_compliance_score\": 95.5, \"exceptions_required\": [], \"policy_version\": \"2024.1\"}\n",
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},
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"kyc_status": "compliant",
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"output": "__OUTPUTS__ {\"kyc_status\": \"compliant\", \"identity_verified\": true, \"address_verified\": true, \"income_verified\": true, \"documents_complete\": true, \"risk_category\": \"low\", \"compliance_score\": 98.5, \"last_updated\": \"2024-07-15\", \"regulatory_requirements_met\": true}\n",
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{
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{
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{
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"address_match": true,
"document_date": "2024-06-15",
"document_type": "utility_bill",
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},
"duration_seconds": 0.735687,
"end_time": "2025-07-15T11:33:51.224529",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.488842",
"timestamp": "2025-07-15T11:33:51.224529",
"worker_executed": true,
"workers_notified": true
},
"is_valid": true,
"output": "__OUTPUTS__ {\"document_type\": \"utility_bill\", \"is_valid\": true, \"address_match\": true, \"document_date\": \"2024-06-15\", \"verification_score\": 92.3, \"address_confirmed\": \"123 Main St, City, State 12345\"}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_address_proof",
"verification_score": 92.3
},
"verify_employment": {
"designation": "Senior Software Engineer",
"employer_rating": "A+",
"employment_confirmed": true,
"employment_type": "permanent",
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"employment_confirmed\": true, \"designation\": \"Senior Software Engineer\", \"employment_type\": \"permanent\", \"probation_status\": \"confirmed\", \"employer_rating\": \"A+\", \"job_stability_score\": 88.5, \"reference_check_passed\": true, \"hr_contact_verified\": true}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.64096,
"end_time": "2025-07-15T11:33:53.955164",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.314204",
"timestamp": "2025-07-15T11:33:53.955164",
"worker_executed": true,
"workers_notified": true
},
"hr_contact_verified": true,
"job_stability_score": 88.5,
"output": "__OUTPUTS__ {\"employment_confirmed\": true, \"designation\": \"Senior Software Engineer\", \"employment_type\": \"permanent\", \"probation_status\": \"confirmed\", \"employer_rating\": \"A+\", \"job_stability_score\": 88.5, \"reference_check_passed\": true, \"hr_contact_verified\": true}\n",
"probation_status": "confirmed",
"reference_check_passed": true,
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_employment"
},
"verify_pan_card": {
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"pan_number\": \"ABCDE1234F\", \"is_valid\": true, \"name_match\": true, \"status\": \"active\", \"verification_score\": 95.5, \"verified_name\": \"JOHN DOE\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.341657,
"end_time": "2025-07-15T11:33:50.713657",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.372000",
"timestamp": "2025-07-15T11:33:50.713657",
"worker_executed": true,
"workers_notified": true
},
"is_valid": true,
"name_match": true,
"output": "__OUTPUTS__ {\"pan_number\": \"ABCDE1234F\", \"is_valid\": true, \"name_match\": true, \"status\": \"active\", \"verification_score\": 95.5, \"verified_name\": \"JOHN DOE\"}\n",
"pan_number": "ABCDE1234F",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_pan_card",
"verification_score": 95.5,
"verified_name": "JOHN DOE"
},
"verify_passport": {
"document_provided": true,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"passport_number\": \"A1234567\", \"is_valid\": true, \"expiry_date\": \"2030-12-31\", \"issuing_authority\": \"Government of India\", \"verification_score\": 96.8, \"document_provided\": true}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.563517,
"end_time": "2025-07-15T11:33:51.013041",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.449524",
"timestamp": "2025-07-15T11:33:51.013041",
"worker_executed": true,
"workers_notified": true
},
"expiry_date": "2030-12-31",
"is_valid": true,
"issuing_authority": "Government of India",
"output": "__OUTPUTS__ {\"passport_number\": \"A1234567\", \"is_valid\": true, \"expiry_date\": \"2030-12-31\", \"issuing_authority\": \"Government of India\", \"verification_score\": 96.8, \"document_provided\": true}\n",
"passport_number": "A1234567",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_passport",
"verification_score": 96.8
},
"verify_tax_returns": {
"declared_income": 98000,
"discrepancies_found": false,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"declared_income\": 98000, \"tax_paid\": 12000, \"returns_filed_consistently\": true, \"income_growth_trend\": \"positive\", \"discrepancies_found\": false, \"verification_with_govt\": true, \"tax_compliance_score\": 92.0}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.856287,
"end_time": "2025-07-15T11:33:53.915962",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.059675",
"timestamp": "2025-07-15T11:33:53.915962",
"worker_executed": true,
"workers_notified": true
},
"income_growth_trend": "positive",
"output": "__OUTPUTS__ {\"declared_income\": 98000, \"tax_paid\": 12000, \"returns_filed_consistently\": true, \"income_growth_trend\": \"positive\", \"discrepancies_found\": false, \"verification_with_govt\": true, \"tax_compliance_score\": 92.0}\n",
"return_code": 0,
"returns_filed_consistently": true,
"status": "completed",
"stderr": "",
"task_id": "verify_tax_returns",
"tax_compliance_score": 92.0,
"tax_paid": 12000,
"verification_with_govt": true
}
},
"underwriting_decision_router": {
"all_conditions_checked": [
{
"condition": "${calculate_credit_score.internal_credit_score} \u003c 600 || ${calculate_debt_to_income.debt_to_income_ratio} \u003e 0.50",
"name": "high_risk_path",
"result": false,
"route": "decline_path"
},
{
"condition": "${calculate_credit_score.internal_credit_score} \u003e= 600 \u0026\u0026 ${calculate_credit_score.internal_credit_score} \u003c 700",
"name": "conditional_approval_path",
"result": false,
"route": "conditional_path"
},
{
"condition": "${calculate_credit_score.internal_credit_score} \u003e= 700",
"name": "standard_approval_path",
"result": false,
"route": "approval_path"
}
],
"duration_seconds": 0.00224,
"ended_at": "2025-07-15T11:37:21.611623",
"evaluation_details": {},
"execution_details": {
"actual_result": {
"output": "Route selected: manual_review_path\n__OUTPUTS__ {\"router_type\": \"condition\", \"selected_route\": \"manual_review_path\", \"route_reason\": \"no_conditions_matched\", \"evaluation_details\": {}, \"all_conditions_checked\": [{\"name\": \"high_risk_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003c 600 || ${calculate_debt_to_income.debt_to_income_ratio} \u003e 0.50\", \"result\": false, \"route\": \"decline_path\"}, {\"name\": \"conditional_approval_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003e= 600 \u0026\u0026 ${calculate_credit_score.internal_credit_score} \u003c 700\", \"result\": false, \"route\": \"conditional_path\"}, {\"name\": \"standard_approval_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003e= 700\", \"result\": false, \"route\": \"approval_path\"}], \"duration_seconds\": 0.00224, \"started_at\": \"2025-07-15T11:37:21.609383\", \"ended_at\": \"2025-07-15T11:37:21.611623\"}",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.299134,
"end_time": "2025-07-15T11:37:21.799112",
"message_sent": true,
"start_time": "2025-07-15T11:37:21.499978",
"timestamp": "2025-07-15T11:37:21.799112",
"worker_executed": true,
"workers_notified": true
},
"output": "Route selected: manual_review_path\n__OUTPUTS__ {\"router_type\": \"condition\", \"selected_route\": \"manual_review_path\", \"route_reason\": \"no_conditions_matched\", \"evaluation_details\": {}, \"all_conditions_checked\": [{\"name\": \"high_risk_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003c 600 || ${calculate_debt_to_income.debt_to_income_ratio} \u003e 0.50\", \"result\": false, \"route\": \"decline_path\"}, {\"name\": \"conditional_approval_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003e= 600 \u0026\u0026 ${calculate_credit_score.internal_credit_score} \u003c 700\", \"result\": false, \"route\": \"conditional_path\"}, {\"name\": \"standard_approval_path\", \"condition\": \"${calculate_credit_score.internal_credit_score} \u003e= 700\", \"result\": false, \"route\": \"approval_path\"}], \"duration_seconds\": 0.00224, \"started_at\": \"2025-07-15T11:37:21.609383\", \"ended_at\": \"2025-07-15T11:37:21.611623\"}",
"return_code": 0,
"route_reason": "no_conditions_matched",
"router_type": "condition",
"selected_route": "manual_review_path",
"started_at": "2025-07-15T11:37:21.609383",
"status": "completed",
"stderr": "",
"task_id": "underwriting_decision_router"
},
"validate_application_fields": {
"completion_percentage": 100.0,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"validation_passed\": true, \"missing_fields\": [], \"completion_percentage\": 100.0}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.229927,
"end_time": "2025-07-15T11:33:46.176918",
"message_sent": true,
"start_time": "2025-07-15T11:33:45.946991",
"timestamp": "2025-07-15T11:33:46.176918",
"worker_executed": true,
"workers_notified": true
},
"missing_fields": [],
"output": "__OUTPUTS__ {\"validation_passed\": true, \"missing_fields\": [], \"completion_percentage\": 100.0}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "validate_application_fields",
"validation_passed": true
},
"verify_aadhaar": {
"aadhaar_masked": "1234-5678-9012",
"address_match": true,
"biometric_verified": true,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"aadhaar_masked\": \"1234-5678-9012\", \"is_valid\": true, \"address_match\": true, \"biometric_verified\": true, \"verification_score\": 98.2, \"last_updated\": \"2024-01-15\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.44117,
"end_time": "2025-07-15T11:33:50.850106",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.408936",
"timestamp": "2025-07-15T11:33:50.850106",
"worker_executed": true,
"workers_notified": true
},
"is_valid": true,
"last_updated": "2024-01-15",
"output": "__OUTPUTS__ {\"aadhaar_masked\": \"1234-5678-9012\", \"is_valid\": true, \"address_match\": true, \"biometric_verified\": true, \"verification_score\": 98.2, \"last_updated\": \"2024-01-15\"}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_aadhaar",
"verification_score": 98.2
},
"verify_address_proof": {
"address_confirmed": "123 Main St, City, State 12345",
"address_match": true,
"document_date": "2024-06-15",
"document_type": "utility_bill",
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"document_type\": \"utility_bill\", \"is_valid\": true, \"address_match\": true, \"document_date\": \"2024-06-15\", \"verification_score\": 92.3, \"address_confirmed\": \"123 Main St, City, State 12345\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.735687,
"end_time": "2025-07-15T11:33:51.224529",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.488842",
"timestamp": "2025-07-15T11:33:51.224529",
"worker_executed": true,
"workers_notified": true
},
"is_valid": true,
"output": "__OUTPUTS__ {\"document_type\": \"utility_bill\", \"is_valid\": true, \"address_match\": true, \"document_date\": \"2024-06-15\", \"verification_score\": 92.3, \"address_confirmed\": \"123 Main St, City, State 12345\"}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_address_proof",
"verification_score": 92.3
},
"verify_employment": {
"designation": "Senior Software Engineer",
"employer_rating": "A+",
"employment_confirmed": true,
"employment_type": "permanent",
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"employment_confirmed\": true, \"designation\": \"Senior Software Engineer\", \"employment_type\": \"permanent\", \"probation_status\": \"confirmed\", \"employer_rating\": \"A+\", \"job_stability_score\": 88.5, \"reference_check_passed\": true, \"hr_contact_verified\": true}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.64096,
"end_time": "2025-07-15T11:33:53.955164",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.314204",
"timestamp": "2025-07-15T11:33:53.955164",
"worker_executed": true,
"workers_notified": true
},
"hr_contact_verified": true,
"job_stability_score": 88.5,
"output": "__OUTPUTS__ {\"employment_confirmed\": true, \"designation\": \"Senior Software Engineer\", \"employment_type\": \"permanent\", \"probation_status\": \"confirmed\", \"employer_rating\": \"A+\", \"job_stability_score\": 88.5, \"reference_check_passed\": true, \"hr_contact_verified\": true}\n",
"probation_status": "confirmed",
"reference_check_passed": true,
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_employment"
},
"verify_pan_card": {
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"pan_number\": \"ABCDE1234F\", \"is_valid\": true, \"name_match\": true, \"status\": \"active\", \"verification_score\": 95.5, \"verified_name\": \"JOHN DOE\"}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.341657,
"end_time": "2025-07-15T11:33:50.713657",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.372000",
"timestamp": "2025-07-15T11:33:50.713657",
"worker_executed": true,
"workers_notified": true
},
"is_valid": true,
"name_match": true,
"output": "__OUTPUTS__ {\"pan_number\": \"ABCDE1234F\", \"is_valid\": true, \"name_match\": true, \"status\": \"active\", \"verification_score\": 95.5, \"verified_name\": \"JOHN DOE\"}\n",
"pan_number": "ABCDE1234F",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_pan_card",
"verification_score": 95.5,
"verified_name": "JOHN DOE"
},
"verify_passport": {
"document_provided": true,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"passport_number\": \"A1234567\", \"is_valid\": true, \"expiry_date\": \"2030-12-31\", \"issuing_authority\": \"Government of India\", \"verification_score\": 96.8, \"document_provided\": true}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.563517,
"end_time": "2025-07-15T11:33:51.013041",
"message_sent": true,
"start_time": "2025-07-15T11:33:50.449524",
"timestamp": "2025-07-15T11:33:51.013041",
"worker_executed": true,
"workers_notified": true
},
"expiry_date": "2030-12-31",
"is_valid": true,
"issuing_authority": "Government of India",
"output": "__OUTPUTS__ {\"passport_number\": \"A1234567\", \"is_valid\": true, \"expiry_date\": \"2030-12-31\", \"issuing_authority\": \"Government of India\", \"verification_score\": 96.8, \"document_provided\": true}\n",
"passport_number": "A1234567",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "verify_passport",
"verification_score": 96.8
},
"verify_tax_returns": {
"declared_income": 98000,
"discrepancies_found": false,
"execution_details": {
"actual_result": {
"output": "__OUTPUTS__ {\"declared_income\": 98000, \"tax_paid\": 12000, \"returns_filed_consistently\": true, \"income_growth_trend\": \"positive\", \"discrepancies_found\": false, \"verification_with_govt\": true, \"tax_compliance_score\": 92.0}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.856287,
"end_time": "2025-07-15T11:33:53.915962",
"message_sent": true,
"start_time": "2025-07-15T11:33:53.059675",
"timestamp": "2025-07-15T11:33:53.915962",
"worker_executed": true,
"workers_notified": true
},
"income_growth_trend": "positive",
"output": "__OUTPUTS__ {\"declared_income\": 98000, \"tax_paid\": 12000, \"returns_filed_consistently\": true, \"income_growth_trend\": \"positive\", \"discrepancies_found\": false, \"verification_with_govt\": true, \"tax_compliance_score\": 92.0}\n",
"return_code": 0,
"returns_filed_consistently": true,
"status": "completed",
"stderr": "",
"task_id": "verify_tax_returns",
"tax_compliance_score": 92.0,
"tax_paid": 12000,
"verification_with_govt": true
}
}