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": 0.311946,
"end_time": "2025-07-15T11:32:04.101195",
"message_sent": true,
"start_time": "2025-07-15T11:32:03.789249",
"timestamp": "2025-07-15T11:32:04.101195",
"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": 0.761984,
"end_time": "2025-07-15T11:28:41.527705",
"message_sent": true,
"start_time": "2025-07-15T11:28:40.765721",
"timestamp": "2025-07-15T11:28:41.527705",
"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": 0.252522,
"end_time": "2025-07-15T11:28:49.677706",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.425184",
"timestamp": "2025-07-15T11:28:49.677706",
"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": 0.324125,
"end_time": "2025-07-15T11:28:41.048636",
"message_sent": true,
"start_time": "2025-07-15T11:28:40.724511",
"timestamp": "2025-07-15T11:28:41.048636",
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 1090,
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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, indicating a high level of accuracy.\\\"\\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 6.107947,
"end_time": "2025-07-15T11:28:38.743829",
"message_sent": true,
"start_time": "2025-07-15T11:28:32.635882",
"timestamp": "2025-07-15T11:28:38.743829",
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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, indicating a high level of accuracy.\\\"\\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 0.272525,
"end_time": "2025-07-15T11:28:49.981080",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.708555",
"timestamp": "2025-07-15T11:28:49.981080",
"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/tmpmnb06tw2.py\", line 11, in \u003cmodule\u003e\n credit_utilization = UNRESOLVED_analyze_credit_history.credit_limit_utilization\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_analyze_credit_history\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/tmpmnb06tw2.py\", line 11, in \u003cmodule\u003e\n credit_utilization = UNRESOLVED_analyze_credit_history.credit_limit_utilization\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_analyze_credit_history\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": 0.418721,
"end_time": "2025-07-15T11:28:41.876612",
"message_sent": true,
"start_time": "2025-07-15T11:28:41.457891",
"timestamp": "2025-07-15T11:28:41.876612",
"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": 0.276652,
"end_time": "2025-07-15T11:28:49.467977",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.191325",
"timestamp": "2025-07-15T11:28:49.467977",
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 571,
"model": "gpt-4o-mini",
"output_length": 1167,
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"KYC\\\": \\\"High risk due to unresolved KYC compliance check.\\\",\\n \\\"AML\\\": \\\"High risk due to unresolved AML screening.\\\",\\n \\\"Regulatory\\\": \\\"High risk due to unresolved regulatory compliance.\\\",\\n \\\"Internal Policy\\\": \\\"Low risk; internal policy compliance score is high.\\\"\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n \\\"Unresolved KYC compliance check poses a significant risk of identity fraud.\\\",\\n \\\"Unresolved AML screening increases the risk of money laundering activities.\\\",\\n \\\"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\\\"\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\\\",\\n \\\"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\\\",\\n \\\"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\\\",\\n \\\"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 25\\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": 5.770626,
"end_time": "2025-07-15T11:32:10.352190",
"message_sent": true,
"start_time": "2025-07-15T11:32:04.581564",
"timestamp": "2025-07-15T11:32:10.352190",
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"KYC\\\": \\\"High risk due to unresolved KYC compliance check.\\\",\\n \\\"AML\\\": \\\"High risk due to unresolved AML screening.\\\",\\n \\\"Regulatory\\\": \\\"High risk due to unresolved regulatory compliance.\\\",\\n \\\"Internal Policy\\\": \\\"Low risk; internal policy compliance score is high.\\\"\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n \\\"Unresolved KYC compliance check poses a significant risk of identity fraud.\\\",\\n \\\"Unresolved AML screening increases the risk of money laundering activities.\\\",\\n \\\"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\\\"\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\\\",\\n \\\"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\\\",\\n \\\"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\\\",\\n \\\"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 25\\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": 2.54955,
"end_time": "2025-07-15T11:28:41.224602",
"message_sent": true,
"start_time": "2025-07-15T11:28:38.675052",
"timestamp": "2025-07-15T11:28:41.224602",
"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 and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.",
"execution_details": {
"actual_result": {
"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 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 and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\\u2019s a breakdown of the analysis based on the information provided:\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score,\\\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\\n- **Credit History**: Similarly, the credit history is marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is described as \\\"excellent.\\\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: The borrower has two loans:\\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\\n\\n### Risk Score Calculation\\nGiven the available data, we can assign a risk score based on the factors analyzed:\\n\\n- Excellent repayment track record: -10 points (reduces risk)\\n- Loan burden ratio of 28%: -5 points (reduces risk)\\n- Unknown CIBIL score and credit history: +30 points (increases risk)\\n\\n### Final Risk Score\\n- Starting from a baseline of 50 (neutral risk):\\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\\n- Final Risk Score = 50 + 15 = **65**\\n\\n### Recommendations\\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\\n\\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
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"status": "completed",
"stderr": ""
},
"duration_seconds": 15.301043,
"end_time": "2025-07-15T11:29:05.230145",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.929102",
"timestamp": "2025-07-15T11:29:05.230145",
"worker_executed": true,
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},
"model": "gpt-4o-mini",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\\u2019s a breakdown of the analysis based on the information provided:\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score,\\\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\\n- **Credit History**: Similarly, the credit history is marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is described as \\\"excellent.\\\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: The borrower has two loans:\\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\\n\\n### Risk Score Calculation\\nGiven the available data, we can assign a risk score based on the factors analyzed:\\n\\n- Excellent repayment track record: -10 points (reduces risk)\\n- Loan burden ratio of 28%: -5 points (reduces risk)\\n- Unknown CIBIL score and credit history: +30 points (increases risk)\\n\\n### Final Risk Score\\n- Starting from a baseline of 50 (neutral risk):\\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\\n- Final Risk Score = 50 + 15 = **65**\\n\\n### Recommendations\\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\\n\\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
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},
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"total_tasks": 28
},
"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": 0.28617,
"end_time": "2025-07-15T11:28:32.406819",
"message_sent": true,
"start_time": "2025-07-15T11:28:32.120649",
"timestamp": "2025-07-15T11:28:32.406819",
"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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},
"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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\n\n5. **Confidence Level**: 95\n\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.",
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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"
},
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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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\n\n5. **Confidence Level**: 95\n\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\\n\\n5. **Confidence Level**: 95\\n\\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"ai_response": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\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\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\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\\\": \\\"High stability due to permanent employment and confirmed probation status.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring for any changes in employment status.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true,\\n \\\"assessment\\\": \\\"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"recommendation\\\": \\\"Maintain current debt levels and avoid additional high-interest debt.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"assessment\\\": \\\"Financial discipline appears to be good, as existing debt payments are manageable within the income.\\\",\\n \\\"recommendation\\\": \\\"Continue to budget effectively and prioritize savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low risk due to stable employment and acceptable debt-to-income ratio.\\\",\\n \\\"potential_risks\\\": [\\n \\\"Economic downturn affecting job stability.\\\",\\n \\\"Unexpected expenses that could impact cash flow.\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Establish an emergency fund to cover at least 3-6 months of expenses.\\\",\\n \\\"Consider diversifying income sources if possible.\\\"\\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\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\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\\\": \\\"High stability due to permanent employment and confirmed probation status.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring for any changes in employment status.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true,\\n \\\"assessment\\\": \\\"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"recommendation\\\": \\\"Maintain current debt levels and avoid additional high-interest debt.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"assessment\\\": \\\"Financial discipline appears to be good, as existing debt payments are manageable within the income.\\\",\\n \\\"recommendation\\\": \\\"Continue to budget effectively and prioritize savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low risk due to stable employment and acceptable debt-to-income ratio.\\\",\\n \\\"potential_risks\\\": [\\n \\\"Economic downturn affecting job stability.\\\",\\n \\\"Unexpected expenses that could impact cash flow.\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Establish an emergency fund to cover at least 3-6 months of expenses.\\\",\\n \\\"Consider diversifying income sources if possible.\\\"\\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",
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"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": 0.324125,
"end_time": "2025-07-15T11:28:41.048636",
"message_sent": true,
"start_time": "2025-07-15T11:28:40.724511",
"timestamp": "2025-07-15T11:28:41.048636",
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 1090,
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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, indicating a high level of accuracy.\\\"\\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 6.107947,
"end_time": "2025-07-15T11:28:38.743829",
"message_sent": true,
"start_time": "2025-07-15T11:28:32.635882",
"timestamp": "2025-07-15T11:28:38.743829",
"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, indicating a high level of accuracy.\"\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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, indicating a high level of accuracy.\\\"\\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 were identified based on the provided validation results. However, further details on the 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 the successful validation contribute positively to the overall score. 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": 0.272525,
"end_time": "2025-07-15T11:28:49.981080",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.708555",
"timestamp": "2025-07-15T11:28:49.981080",
"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/tmpmnb06tw2.py\", line 11, in \u003cmodule\u003e\n credit_utilization = UNRESOLVED_analyze_credit_history.credit_limit_utilization\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_analyze_credit_history\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/tmpmnb06tw2.py\", line 11, in \u003cmodule\u003e\n credit_utilization = UNRESOLVED_analyze_credit_history.credit_limit_utilization\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_analyze_credit_history\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": 0.418721,
"end_time": "2025-07-15T11:28:41.876612",
"message_sent": true,
"start_time": "2025-07-15T11:28:41.457891",
"timestamp": "2025-07-15T11:28:41.876612",
"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": 0.276652,
"end_time": "2025-07-15T11:28:49.467977",
"message_sent": true,
"start_time": "2025-07-15T11:28:49.191325",
"timestamp": "2025-07-15T11:28:49.467977",
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 571,
"model": "gpt-4o-mini",
"output_length": 1167,
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"KYC\\\": \\\"High risk due to unresolved KYC compliance check.\\\",\\n \\\"AML\\\": \\\"High risk due to unresolved AML screening.\\\",\\n \\\"Regulatory\\\": \\\"High risk due to unresolved regulatory compliance.\\\",\\n \\\"Internal Policy\\\": \\\"Low risk; internal policy compliance score is high.\\\"\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n \\\"Unresolved KYC compliance check poses a significant risk of identity fraud.\\\",\\n \\\"Unresolved AML screening increases the risk of money laundering activities.\\\",\\n \\\"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\\\"\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\\\",\\n \\\"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\\\",\\n \\\"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\\\",\\n \\\"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 25\\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": 5.770626,
"end_time": "2025-07-15T11:32:10.352190",
"message_sent": true,
"start_time": "2025-07-15T11:32:04.581564",
"timestamp": "2025-07-15T11:32:10.352190",
"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 \"KYC\": \"High risk due to unresolved KYC compliance check.\",\n \"AML\": \"High risk due to unresolved AML screening.\",\n \"Regulatory\": \"High risk due to unresolved regulatory compliance.\",\n \"Internal Policy\": \"Low risk; internal policy compliance score is high.\"\n },\n \"compliance_gaps_or_concerns\": [\n \"Unresolved KYC compliance check poses a significant risk of identity fraud.\",\n \"Unresolved AML screening increases the risk of money laundering activities.\",\n \"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\"\n ],\n \"recommendations_for_proceeding\": [\n \"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\",\n \"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\",\n \"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\",\n \"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\"\n ],\n \"compliance_confidence_score\": 25\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"overall_compliance_status\\\": \\\"Non-compliant\\\",\\n \\\"risk_assessment\\\": {\\n \\\"KYC\\\": \\\"High risk due to unresolved KYC compliance check.\\\",\\n \\\"AML\\\": \\\"High risk due to unresolved AML screening.\\\",\\n \\\"Regulatory\\\": \\\"High risk due to unresolved regulatory compliance.\\\",\\n \\\"Internal Policy\\\": \\\"Low risk; internal policy compliance score is high.\\\"\\n },\\n \\\"compliance_gaps_or_concerns\\\": [\\n \\\"Unresolved KYC compliance check poses a significant risk of identity fraud.\\\",\\n \\\"Unresolved AML screening increases the risk of money laundering activities.\\\",\\n \\\"Unresolved regulatory compliance could lead to legal penalties and reputational damage.\\\"\\n ],\\n \\\"recommendations_for_proceeding\\\": [\\n \\\"Immediately address and resolve the KYC compliance check to mitigate identity fraud risk.\\\",\\n \\\"Conduct a thorough review and resolution of the AML screening to ensure compliance with anti-money laundering regulations.\\\",\\n \\\"Resolve any outstanding regulatory compliance issues to avoid potential legal repercussions.\\\",\\n \\\"Continue to monitor internal policy adherence, as it currently shows a high compliance score.\\\"\\n ],\\n \\\"compliance_confidence_score\\\": 25\\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": 2.54955,
"end_time": "2025-07-15T11:28:41.224602",
"message_sent": true,
"start_time": "2025-07-15T11:28:38.675052",
"timestamp": "2025-07-15T11:28:41.224602",
"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 and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.",
"execution_details": {
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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 and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\\u2019s a breakdown of the analysis based on the information provided:\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score,\\\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\\n- **Credit History**: Similarly, the credit history is marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is described as \\\"excellent.\\\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: The borrower has two loans:\\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\\n\\n### Risk Score Calculation\\nGiven the available data, we can assign a risk score based on the factors analyzed:\\n\\n- Excellent repayment track record: -10 points (reduces risk)\\n- Loan burden ratio of 28%: -5 points (reduces risk)\\n- Unknown CIBIL score and credit history: +30 points (increases risk)\\n\\n### Final Risk Score\\n- Starting from a baseline of 50 (neutral risk):\\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\\n- Final Risk Score = 50 + 15 = **65**\\n\\n### Recommendations\\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\\n\\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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},
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"end_time": "2025-07-15T11:29:05.230145",
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"start_time": "2025-07-15T11:28:49.929102",
"timestamp": "2025-07-15T11:29:05.230145",
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},
"model": "gpt-4o-mini",
"model_client_id": "risk_assessor",
"output": "To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\u2019s a breakdown of the analysis based on the information provided:\n\n### 1. Credit Score and History\n- **CIBIL Score**: The score is marked as \"UNRESOLVED_check_cibil_score,\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\n- **Credit History**: Similarly, the credit history is marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is described as \"excellent.\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: The borrower has two loans:\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\n\n### Risk Score Calculation\nGiven the available data, we can assign a risk score based on the factors analyzed:\n\n- Excellent repayment track record: -10 points (reduces risk)\n- Loan burden ratio of 28%: -5 points (reduces risk)\n- Unknown CIBIL score and credit history: +30 points (increases risk)\n\n### Final Risk Score\n- Starting from a baseline of 50 (neutral risk):\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\n- Final Risk Score = 50 + 15 = **65**\n\n### Recommendations\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\n\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors outlined in your request. Here\\u2019s a breakdown of the analysis based on the information provided:\\n\\n### 1. Credit Score and History\\n- **CIBIL Score**: The score is marked as \\\"UNRESOLVED_check_cibil_score,\\\" which means we cannot assess the creditworthiness based on this crucial metric. A low score would typically indicate higher risk, while a high score would suggest lower risk.\\n- **Credit History**: Similarly, the credit history is marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot evaluate the length of credit history, types of credit accounts, or any past delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is described as \\\"excellent.\\\" This suggests that the borrower has consistently made payments on time, which is a positive indicator and reduces default risk.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which indicates that 28% of the borrower\u0027s income is going towards servicing existing loans. This is generally considered a manageable level, as a ratio below 30% is often viewed favorably.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which needs to be assessed against the borrower\u0027s income to determine affordability. However, without income data, we cannot fully evaluate this aspect.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: The borrower has two loans:\\n - Personal Loan: EMI of 3500, outstanding of 45000, remaining tenure of 18 months.\\n - Auto Loan: EMI of 4200, outstanding of 80000, remaining tenure of 24 months.\\n- The total outstanding amount is 125000, which is significant but manageable given the repayment track record.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: The lack of a CIBIL score and credit history details is a significant gap in the analysis. However, the excellent repayment track record and manageable loan burden ratio suggest that the borrower is currently in a stable financial position.\\n- **Potential Risks**: The unknowns regarding the credit score and history could indicate potential risks. If the borrower has a history of late payments or defaults, this could elevate the risk score.\\n\\n### Risk Score Calculation\\nGiven the available data, we can assign a risk score based on the factors analyzed:\\n\\n- Excellent repayment track record: -10 points (reduces risk)\\n- Loan burden ratio of 28%: -5 points (reduces risk)\\n- Unknown CIBIL score and credit history: +30 points (increases risk)\\n\\n### Final Risk Score\\n- Starting from a baseline of 50 (neutral risk):\\n - Adjustments: -10 (repayment) - 5 (burden) + 30 (unknowns) = 15\\n- Final Risk Score = 50 + 15 = **65**\\n\\n### Recommendations\\n1. **Resolve Credit Score and History**: Obtain the CIBIL score and detailed credit history to better assess the risk. This is crucial for a comprehensive evaluation.\\n2. **Monitor Loan Payments**: Continue maintaining the excellent repayment track record to further improve the credit profile.\\n3. **Consider Reducing Debt**: If possible, focus on paying down existing loans to lower the total outstanding amount and improve the loan burden ratio.\\n4. **Financial Planning**: Ensure that monthly income is sufficient to cover EMIs comfortably, ideally keeping the loan burden ratio below 25% for added safety.\\n\\nIn conclusion, while the borrower currently shows a manageable risk profile, the unknowns regarding the credit score and history necessitate further investigation to ensure a complete risk assessment.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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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
},
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"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",
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"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",
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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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\n\n5. **Confidence Level**: 95\n\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.",
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"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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\n\n5. **Confidence Level**: 95\n\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\\n\\n5. **Confidence Level**: 95\\n\\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\", \"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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\n\n5. **Confidence Level**: 95\n\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\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**: The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score, default risk evaluation, compliance, and debt-to-income calculations. Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant uncertainty regarding the applicant\u0027s financial stability, creditworthiness, and overall risk profile. Without clear and verified information, the risk of default is heightened, and compliance with lending regulations cannot be assured. Therefore, it is prudent to decline the application to mitigate potential financial losses and ensure adherence to lending standards.\\n\\n5. **Confidence Level**: 95\\n\\nThis high confidence level reflects the clear and significant risks associated with proceeding under such unresolved conditions, which could lead to adverse outcomes for both the lender and the borrower.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
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"flag_for_manual_review": {
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"status": "FAILED",
"task_id": "flag_for_manual_review"
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"generate_final_output": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmpnu7r6hii.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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"output": "Task failed: Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmpnu7r6hii.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",
"task_id": "generate_final_output"
},
"income_assessment": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\n ]\n },\n \"overall_income_adequacy_score\": 85\n }\n}\n```",
"execution_details": {
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"agent_type": "assistant",
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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",
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"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\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\\\": \\\"High stability due to permanent employment and confirmed probation status.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring for any changes in employment status.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true,\\n \\\"assessment\\\": \\\"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"recommendation\\\": \\\"Maintain current debt levels and avoid additional high-interest debt.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"assessment\\\": \\\"Financial discipline appears to be good, as existing debt payments are manageable within the income.\\\",\\n \\\"recommendation\\\": \\\"Continue to budget effectively and prioritize savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low risk due to stable employment and acceptable debt-to-income ratio.\\\",\\n \\\"potential_risks\\\": [\\n \\\"Economic downturn affecting job stability.\\\",\\n \\\"Unexpected expenses that could impact cash flow.\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Establish an emergency fund to cover at least 3-6 months of expenses.\\\",\\n \\\"Consider diversifying income sources if possible.\\\"\\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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},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment and confirmed probation status.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"permanent\",\n \"recommendation\": \"Continue monitoring for any changes in employment status.\"\n },\n \"debt_servicing_capability\": {\n \"monthly_net_income\": 6800,\n \"total_debt_payments\": 2441.67,\n \"debt_to_income_ratio\": 0.3591,\n \"max_allowed_ratio\": 0.4,\n \"ratio_acceptable\": true,\n \"assessment\": \"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\",\n \"recommendation\": \"Maintain current debt levels and avoid additional high-interest debt.\"\n },\n \"financial_discipline\": {\n \"existing_debt_payments\": 2400,\n \"proposed_emi\": 41.67,\n \"assessment\": \"Financial discipline appears to be good, as existing debt payments are manageable within the income.\",\n \"recommendation\": \"Continue to budget effectively and prioritize savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Low risk due to stable employment and acceptable debt-to-income ratio.\",\n \"potential_risks\": [\n \"Economic downturn affecting job stability.\",\n \"Unexpected expenses that could impact cash flow.\"\n ],\n \"recommendations\": [\n \"Establish an emergency fund to cover at least 3-6 months of expenses.\",\n \"Consider diversifying income sources if possible.\"\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\\\": \\\"High stability due to permanent employment and confirmed probation status.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring for any changes in employment status.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"monthly_net_income\\\": 6800,\\n \\\"total_debt_payments\\\": 2441.67,\\n \\\"debt_to_income_ratio\\\": 0.3591,\\n \\\"max_allowed_ratio\\\": 0.4,\\n \\\"ratio_acceptable\\\": true,\\n \\\"assessment\\\": \\\"Debt servicing capability is strong, with a debt-to-income ratio below the acceptable threshold.\\\",\\n \\\"recommendation\\\": \\\"Maintain current debt levels and avoid additional high-interest debt.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"existing_debt_payments\\\": 2400,\\n \\\"proposed_emi\\\": 41.67,\\n \\\"assessment\\\": \\\"Financial discipline appears to be good, as existing debt payments are manageable within the income.\\\",\\n \\\"recommendation\\\": \\\"Continue to budget effectively and prioritize savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Low risk due to stable employment and acceptable debt-to-income ratio.\\\",\\n \\\"potential_risks\\\": [\\n \\\"Economic downturn affecting job stability.\\\",\\n \\\"Unexpected expenses that could impact cash flow.\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Establish an emergency fund to cover at least 3-6 months of expenses.\\\",\\n \\\"Consider diversifying income sources if possible.\\\"\\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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},
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{
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{
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"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": 0.746918,
"end_time": "2025-07-15T11:28:41.554516",
"message_sent": true,
"start_time": "2025-07-15T11:28:40.807598",
"timestamp": "2025-07-15T11:28:41.554516",
"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": 0.003653,
"ended_at": "2025-07-15T11:32:10.129835",
"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\": 0.003653, \"started_at\": \"2025-07-15T11:32:10.126182\", \"ended_at\": \"2025-07-15T11:32:10.129835\"}",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.202417,
"end_time": "2025-07-15T11:32:10.235763",
"message_sent": true,
"start_time": "2025-07-15T11:32:10.033346",
"timestamp": "2025-07-15T11:32:10.235763",
"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\": 0.003653, \"started_at\": \"2025-07-15T11:32:10.126182\", \"ended_at\": \"2025-07-15T11:32:10.129835\"}",
"return_code": 0,
"route_reason": "no_conditions_matched",
"router_type": "condition",
"selected_route": "manual_review_path",
"started_at": "2025-07-15T11:32:10.126182",
"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": 0.316699,
"end_time": "2025-07-15T11:28:32.678907",
"message_sent": true,
"start_time": "2025-07-15T11:28:32.362208",
"timestamp": "2025-07-15T11:28:32.678907",
"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": 0.514147,
"end_time": "2025-07-15T11:28:38.290095",
"message_sent": true,
"start_time": "2025-07-15T11:28:37.775948",
"timestamp": "2025-07-15T11:28:38.290095",
"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": 0.857569,
"end_time": "2025-07-15T11:28:38.717576",
"message_sent": true,
"start_time": "2025-07-15T11:28:37.860007",
"timestamp": "2025-07-15T11:28:38.717576",
"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": 0.537636,
"end_time": "2025-07-15T11:28:41.626196",
"message_sent": true,
"start_time": "2025-07-15T11:28:41.088560",
"timestamp": "2025-07-15T11:28:41.626196",
"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": 0.395612,
"end_time": "2025-07-15T11:28:38.129878",
"message_sent": true,
"start_time": "2025-07-15T11:28:37.734266",
"timestamp": "2025-07-15T11:28:38.129878",
"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": 0.641577,
"end_time": "2025-07-15T11:28:38.460049",
"message_sent": true,
"start_time": "2025-07-15T11:28:37.818472",
"timestamp": "2025-07-15T11:28:38.460049",
"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": 0.746918,
"end_time": "2025-07-15T11:28:41.554516",
"message_sent": true,
"start_time": "2025-07-15T11:28:40.807598",
"timestamp": "2025-07-15T11:28:41.554516",
"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
}
}