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": 0.306727,
"end_time": "2025-07-15T11:13:57.536561",
"message_sent": true,
"start_time": "2025-07-15T11:13:57.229834",
"timestamp": "2025-07-15T11:13:57.536561",
"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": 0.589868,
"end_time": "2025-07-15T11:07:32.827993",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.238125",
"timestamp": "2025-07-15T11:07:32.827993",
"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": 0.322589,
"end_time": "2025-07-15T11:10:41.452913",
"message_sent": true,
"start_time": "2025-07-15T11:10:41.130324",
"timestamp": "2025-07-15T11:10:41.452913",
"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": 0.491258,
"end_time": "2025-07-15T11:07:32.682182",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.190924",
"timestamp": "2025-07-15T11:07:32.682182",
"worker_executed": true,
"workers_notified": true
},
"monthly_gross": 8500,
"monthly_net": 6800,
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"recent_increment": true,
"return_code": 0,
"salary_consistency": true,
"status": "completed",
"stderr": "",
"task_id": "analyze_salary_slips"
},
"application_quality_check": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 246,
"model": "gpt-4o-mini",
"output_length": 1035,
"provider": "openai",
"system_message": "You are an expert loan underwriting analyst. Review the application data and assess its quality.\n\nEvaluate:\n1. Data completeness and accuracy\n2. Consistency of information\n3. Red flags or inconsistencies\n4. Overall application quality score (1-100)\n\nReturn a JSON response with your assessment.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\\\"\\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": 3.788084,
"end_time": "2025-07-15T11:07:29.233284",
"message_sent": true,
"start_time": "2025-07-15T11:07:25.445200",
"timestamp": "2025-07-15T11:07:29.233284",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\\\"\\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": 0.341955,
"end_time": "2025-07-15T11:10:41.732435",
"message_sent": true,
"start_time": "2025-07-15T11:10:41.390480",
"timestamp": "2025-07-15T11:10:41.732435",
"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/tmpob3v9qss.py\", line 9, in \u003cmodule\u003e\n cibil_score = UNRESOLVED_check_cibil_score.credit_score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_check_cibil_score\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/tmpob3v9qss.py\", line 9, in \u003cmodule\u003e\n cibil_score = UNRESOLVED_check_cibil_score.credit_score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_check_cibil_score\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_credit_score"
},
"calculate_debt_to_income": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmp9uazrxgv.py\", line 20, in \u003cmodule\u003e\n max_allowed_ratio = UNRESOLVED_variables.max_debt_to_income_ratio\n ^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_variables\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/tmp9uazrxgv.py\", line 20, in \u003cmodule\u003e\n max_allowed_ratio = UNRESOLVED_variables.max_debt_to_income_ratio\n ^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_variables\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_debt_to_income"
},
"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": 0.249685,
"end_time": "2025-07-15T11:10:41.097705",
"message_sent": true,
"start_time": "2025-07-15T11:10:40.848020",
"timestamp": "2025-07-15T11:10:41.097705",
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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": 1128,
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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 \\\"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\\\",\\n \\\"Address AML screening concerns 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 strong compliance.\\\"\\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": 5.179122,
"end_time": "2025-07-15T11:14:03.230107",
"message_sent": true,
"start_time": "2025-07-15T11:13:58.050985",
"timestamp": "2025-07-15T11:14:03.230107",
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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 \\\"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\\\",\\n \\\"Address AML screening concerns 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 strong compliance.\\\"\\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 \"biometric_verification\": 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": 372,
"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 \"biometric_verification\": 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 \\\"biometric_verification\\\": 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": 2.949249,
"end_time": "2025-07-15T11:07:33.167017",
"message_sent": true,
"start_time": "2025-07-15T11:07:30.217768",
"timestamp": "2025-07-15T11:07:33.167017",
"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 \"biometric_verification\": 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 \\\"biometric_verification\\\": 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 mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
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"model": "gpt-4o-mini",
"output_length": 3134,
"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 mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\\n- **Credit History**: The history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot determine the length of credit history or any delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: \\n - Personal Loan: \\u20b945,000 with an EMI of \\u20b93,500 and a remaining tenure of 18 months.\\n - Auto Loan: \\u20b980,000 with an EMI of \\u20b94,200 and a remaining tenure of 24 months.\\n- **Total Outstanding**: \\u20b9125,000 across two loans.\\n- **Total Monthly EMI**: \\u20b97,700, which is manageable if the borrower\u0027s income supports it.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: \\n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\\n \\n### Risk Score Calculation\\nGiven the information provided, we can assign a risk score based on the following criteria:\\n- **CIBIL Score**: Unknown (high risk)\\n- **Repayment Track Record**: Excellent (low risk)\\n- **Loan Burden Ratio**: 28% (low risk)\\n- **Outstanding Loans**: Manageable (low risk)\\n\\n### Risk Score\\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\\n- Excellent repayment history and reasonable loan burden will lower the risk score.\\n\\n**Final Risk Score: 65/100**\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\\n\\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 16.867303,
"end_time": "2025-07-15T11:10:58.551651",
"message_sent": true,
"start_time": "2025-07-15T11:10:41.684348",
"timestamp": "2025-07-15T11:10:58.551651",
"worker_executed": true,
"workers_notified": true
},
"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 mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\\n- **Credit History**: The history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot determine the length of credit history or any delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: \\n - Personal Loan: \\u20b945,000 with an EMI of \\u20b93,500 and a remaining tenure of 18 months.\\n - Auto Loan: \\u20b980,000 with an EMI of \\u20b94,200 and a remaining tenure of 24 months.\\n- **Total Outstanding**: \\u20b9125,000 across two loans.\\n- **Total Monthly EMI**: \\u20b97,700, which is manageable if the borrower\u0027s income supports it.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: \\n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\\n \\n### Risk Score Calculation\\nGiven the information provided, we can assign a risk score based on the following criteria:\\n- **CIBIL Score**: Unknown (high risk)\\n- **Repayment Track Record**: Excellent (low risk)\\n- **Loan Burden Ratio**: 28% (low risk)\\n- **Outstanding Loans**: Manageable (low risk)\\n\\n### Risk Score\\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\\n- Excellent repayment history and reasonable loan burden will lower the risk score.\\n\\n**Final Risk Score: 65/100**\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\\n\\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"provider": "openai",
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"status": "completed",
"stderr": "",
"task_id": "evaluate_default_risk"
},
"execution_summary": {
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},
"extract_application_info": {
"customer_info": {
"address": "123 Main St, City, State 12345",
"annual_income": 75000,
"customer_id": "CUST_12345",
"email": "john.doe@email.com",
"employment_status": "employed",
"full_name": "John Doe",
"loan_purpose": "home_purchase",
"phone": "+1-555-0123",
"requested_amount": 1000
},
"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\": 1000}, \"missing_fields\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.299819,
"end_time": "2025-07-15T11:07:25.206374",
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"start_time": "2025-07-15T11:07:24.906555",
"timestamp": "2025-07-15T11:07:25.206374",
"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\": 1000}, \"missing_fields\": []}\n",
"return_code": 0,
"status": "completed",
"stderr": "",
"task_id": "extract_application_info"
},
"final_underwriting_analysis": {
"agent_type": "assistant",
"ai_response": "1. **Final Recommendation: DECLINE**\n\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.",
"execution_details": {
"actual_result": {
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"execution_details": {
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"model": "gpt-4o-mini",
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"provider": "openai",
"system_message": "You are a senior underwriting manager making final loan decisions.\n\nAnalyze all assessment results and provide:\n1. Final recommendation (APPROVE/CONDITIONAL/DECLINE)\n2. Loan terms and conditions\n3. Risk mitigation measures\n4. Reasoning for decision\n5. Confidence level (1-100)\n\nConsider all factors: income, credit, compliance, and overall risk profile.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "1. **Final Recommendation: DECLINE**\n\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation: DECLINE**\\n\\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\\n\\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\\n\\n4. **Reasoning for Decision:**\\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \\n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\\n\\n5. **Confidence Level: 95**\\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
"output_format": "json",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 4.952704,
"end_time": "2025-07-15T11:14:08.045212",
"message_sent": true,
"start_time": "2025-07-15T11:14:03.092508",
"timestamp": "2025-07-15T11:14:08.045212",
"worker_executed": true,
"workers_notified": true
},
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"output": "1. **Final Recommendation: DECLINE**\n\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation: DECLINE**\\n\\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\\n\\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\\n\\n4. **Reasoning for Decision:**\\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \\n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\\n\\n5. **Confidence Level: 95**\\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"ai_response": "Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\n\n```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment status and confirmed probation.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"Permanent\",\n \"recommendation\": \"Continue monitoring employment status and ensure ongoing performance.\"\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Unable to calculate due to debt-to-income ratio script failure.\",\n \"debt_to_income_ratio\": \"Error in calculation\",\n \"recommendation\": \"Resolve the calculation error to assess debt servicing capability accurately.\"\n },\n \"financial_discipline\": {\n \"assessment\": \"Requires further analysis of bank statements and salary slips.\",\n \"bank_statement_analysis\": \"Pending\",\n \"salary_analysis\": \"Pending\",\n \"recommendation\": \"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\",\n \"identified_risks\": [\n \"Uncertainty in debt servicing capability\",\n \"Pending verification of income sources\",\n \"Dependence on a single income source\"\n ],\n \"recommendations\": [\n \"Resolve the debt-to-income calculation error.\",\n \"Collect and analyze salary slips and bank statements.\",\n \"Consider diversifying income sources to mitigate risk.\"\n ]\n },\n \"overall_income_adequacy_score\": {\n \"score\": 75,\n \"justification\": \"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\"\n }\n }\n}\n```\n\n### Explanation of the Assessment:\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\n \n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\n\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\n\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. Recommendations include resolving the calculation error and obtaining necessary financial documents.\n\n5. **Overall Income Adequacy Score**: A score of 75 reflects a generally stable income situation but highlights the need for further analysis to fully understand the individual\u0027s financial health.",
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"output": "Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\n\n```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment status and confirmed probation.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"Permanent\",\n \"recommendation\": \"Continue monitoring employment status and ensure ongoing performance.\"\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Unable to calculate due to debt-to-income ratio script failure.\",\n \"debt_to_income_ratio\": \"Error in calculation\",\n \"recommendation\": \"Resolve the calculation error to assess debt servicing capability accurately.\"\n },\n \"financial_discipline\": {\n \"assessment\": \"Requires further analysis of bank statements and salary slips.\",\n \"bank_statement_analysis\": \"Pending\",\n \"salary_analysis\": \"Pending\",\n \"recommendation\": \"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\",\n \"identified_risks\": [\n \"Uncertainty in debt servicing capability\",\n \"Pending verification of income sources\",\n \"Dependence on a single income source\"\n ],\n \"recommendations\": [\n \"Resolve the debt-to-income calculation error.\",\n \"Collect and analyze salary slips and bank statements.\",\n \"Consider diversifying income sources to mitigate risk.\"\n ]\n },\n \"overall_income_adequacy_score\": {\n \"score\": 75,\n \"justification\": \"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\"\n }\n }\n}\n```\n\n### Explanation of the Assessment:\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\n \n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\n\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\n\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. Recommendations include resolving the calculation error and obtaining necessary financial documents.\n\n5. **Overall Income Adequacy Score**: A score of 75 reflects a generally stable income situation but highlights the need for further analysis to fully understand the individual\u0027s financial health.\n__OUTPUTS__ {\"ai_response\": \"Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\\n\\n```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"High stability due to permanent employment status and confirmed probation.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring employment status and ensure ongoing performance.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Unable to calculate due to debt-to-income ratio script failure.\\\",\\n \\\"debt_to_income_ratio\\\": \\\"Error in calculation\\\",\\n \\\"recommendation\\\": \\\"Resolve the calculation error to assess debt servicing capability accurately.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Requires further analysis of bank statements and salary slips.\\\",\\n \\\"bank_statement_analysis\\\": \\\"Pending\\\",\\n \\\"salary_analysis\\\": \\\"Pending\\\",\\n \\\"recommendation\\\": \\\"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\\\",\\n \\\"identified_risks\\\": [\\n \\\"Uncertainty in debt servicing capability\\\",\\n \\\"Pending verification of income sources\\\",\\n \\\"Dependence on a single income source\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Resolve the debt-to-income calculation error.\\\",\\n \\\"Collect and analyze salary slips and bank statements.\\\",\\n \\\"Consider diversifying income sources to mitigate risk.\\\"\\n ]\\n },\\n \\\"overall_income_adequacy_score\\\": {\\n \\\"score\\\": 75,\\n \\\"justification\\\": \\\"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\\\"\\n }\\n }\\n}\\n```\\n\\n### Explanation of the Assessment:\\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\\n \\n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\\n\\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\\n\\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. 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"output": "Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\n\n```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment status and confirmed probation.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"Permanent\",\n \"recommendation\": \"Continue monitoring employment status and ensure ongoing performance.\"\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Unable to calculate due to debt-to-income ratio script failure.\",\n \"debt_to_income_ratio\": \"Error in calculation\",\n \"recommendation\": \"Resolve the calculation error to assess debt servicing capability accurately.\"\n },\n \"financial_discipline\": {\n \"assessment\": \"Requires further analysis of bank statements and salary slips.\",\n \"bank_statement_analysis\": \"Pending\",\n \"salary_analysis\": \"Pending\",\n \"recommendation\": \"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\",\n \"identified_risks\": [\n \"Uncertainty in debt servicing capability\",\n \"Pending verification of income sources\",\n \"Dependence on a single income source\"\n ],\n \"recommendations\": [\n \"Resolve the debt-to-income calculation error.\",\n \"Collect and analyze salary slips and bank statements.\",\n \"Consider diversifying income sources to mitigate risk.\"\n ]\n },\n \"overall_income_adequacy_score\": {\n \"score\": 75,\n \"justification\": \"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\"\n }\n }\n}\n```\n\n### Explanation of the Assessment:\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\n \n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\n\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\n\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. Recommendations include resolving the calculation error and obtaining necessary financial documents.\n\n5. **Overall Income Adequacy Score**: A score of 75 reflects a generally stable income situation but highlights the need for further analysis to fully understand the individual\u0027s financial health.\n__OUTPUTS__ {\"ai_response\": \"Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\\n\\n```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"High stability due to permanent employment status and confirmed probation.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring employment status and ensure ongoing performance.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Unable to calculate due to debt-to-income ratio script failure.\\\",\\n \\\"debt_to_income_ratio\\\": \\\"Error in calculation\\\",\\n \\\"recommendation\\\": \\\"Resolve the calculation error to assess debt servicing capability accurately.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Requires further analysis of bank statements and salary slips.\\\",\\n \\\"bank_statement_analysis\\\": \\\"Pending\\\",\\n \\\"salary_analysis\\\": \\\"Pending\\\",\\n \\\"recommendation\\\": \\\"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\\\",\\n \\\"identified_risks\\\": [\\n \\\"Uncertainty in debt servicing capability\\\",\\n \\\"Pending verification of income sources\\\",\\n \\\"Dependence on a single income source\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Resolve the debt-to-income calculation error.\\\",\\n \\\"Collect and analyze salary slips and bank statements.\\\",\\n \\\"Consider diversifying income sources to mitigate risk.\\\"\\n ]\\n },\\n \\\"overall_income_adequacy_score\\\": {\\n \\\"score\\\": 75,\\n \\\"justification\\\": \\\"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\\\"\\n }\\n }\\n}\\n```\\n\\n### Explanation of the Assessment:\\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\\n \\n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\\n\\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\\n\\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. 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"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": 0.322589,
"end_time": "2025-07-15T11:10:41.452913",
"message_sent": true,
"start_time": "2025-07-15T11:10:41.130324",
"timestamp": "2025-07-15T11:10:41.452913",
"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": 0.491258,
"end_time": "2025-07-15T11:07:32.682182",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.190924",
"timestamp": "2025-07-15T11:07:32.682182",
"worker_executed": true,
"workers_notified": true
},
"monthly_gross": 8500,
"monthly_net": 6800,
"output": "__OUTPUTS__ {\"monthly_gross\": 8500, \"monthly_net\": 6800, \"annual_gross\": 102000, \"annual_net\": 81600, \"employer\": \"Tech Corp Ltd\", \"employment_duration\": \"2.5 years\", \"salary_consistency\": true, \"recent_increment\": true, \"deductions\": {\"tax\": 1200, \"pf\": 500, \"insurance\": 300}}\n",
"recent_increment": true,
"return_code": 0,
"salary_consistency": true,
"status": "completed",
"stderr": "",
"task_id": "analyze_salary_slips"
},
"application_quality_check": {
"agent_type": "assistant",
"ai_response": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```",
"execution_details": {
"actual_result": {
"agent_type": "assistant",
"execution_details": {
"agent_type": "assistant",
"input_length": 246,
"model": "gpt-4o-mini",
"output_length": 1035,
"provider": "openai",
"system_message": "You are an expert loan underwriting analyst. Review the application data and assess its quality.\n\nEvaluate:\n1. Data completeness and accuracy\n2. Consistency of information\n3. Red flags or inconsistencies\n4. Overall application quality score (1-100)\n\nReturn a JSON response with your assessment.\n"
},
"input_format": "json",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\\\"\\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": 3.788084,
"end_time": "2025-07-15T11:07:29.233284",
"message_sent": true,
"start_time": "2025-07-15T11:07:25.445200",
"timestamp": "2025-07-15T11:07:29.233284",
"worker_executed": true,
"workers_notified": true
},
"model": "gpt-4o-mini",
"model_client_id": "underwriting_analyst",
"output": "```json\n{\n \"data_completeness_and_accuracy\": {\n \"missing_fields\": [],\n \"validation_passed\": true,\n \"completion_percentage\": 100.0,\n \"assessment\": \"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\"\n },\n \"consistency_of_information\": {\n \"assessment\": \"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\"\n },\n \"red_flags_or inconsistencies\": {\n \"assessment\": \"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\"\n },\n \"overall_application_quality_score\": 95,\n \"overall_assessment\": \"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\"\n}\n```\n__OUTPUTS__ {\"ai_response\": \"```json\\n{\\n \\\"data_completeness_and_accuracy\\\": {\\n \\\"missing_fields\\\": [],\\n \\\"validation_passed\\\": true,\\n \\\"completion_percentage\\\": 100.0,\\n \\\"assessment\\\": \\\"The application data is complete with no missing fields, and validation checks have passed, indicating that the information provided is accurate.\\\"\\n },\\n \\\"consistency_of_information\\\": {\\n \\\"assessment\\\": \\\"Since there are no missing fields and the validation has passed, the information is likely consistent. However, without specific details on the customer info, a deeper consistency check cannot be performed.\\\"\\n },\\n \\\"red_flags_or inconsistencies\\\": {\\n \\\"assessment\\\": \\\"No red flags or inconsistencies have been identified based on the provided validation results. The application appears to be straightforward.\\\"\\n },\\n \\\"overall_application_quality_score\\\": 95,\\n \\\"overall_assessment\\\": \\\"The application is of high quality due to its completeness and accuracy. However, a more detailed review of the customer information would be necessary to provide a perfect score.\\\"\\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": 0.341955,
"end_time": "2025-07-15T11:10:41.732435",
"message_sent": true,
"start_time": "2025-07-15T11:10:41.390480",
"timestamp": "2025-07-15T11:10:41.732435",
"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/tmpob3v9qss.py\", line 9, in \u003cmodule\u003e\n cibil_score = UNRESOLVED_check_cibil_score.credit_score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_check_cibil_score\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/tmpob3v9qss.py\", line 9, in \u003cmodule\u003e\n cibil_score = UNRESOLVED_check_cibil_score.credit_score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_check_cibil_score\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_credit_score"
},
"calculate_debt_to_income": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmp9uazrxgv.py\", line 20, in \u003cmodule\u003e\n max_allowed_ratio = UNRESOLVED_variables.max_debt_to_income_ratio\n ^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_variables\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/tmp9uazrxgv.py\", line 20, in \u003cmodule\u003e\n max_allowed_ratio = UNRESOLVED_variables.max_debt_to_income_ratio\n ^^^^^^^^^^^^^^^^^^^^\nNameError: name \u0027UNRESOLVED_variables\u0027 is not defined\n",
"status": "FAILED",
"task_id": "calculate_debt_to_income"
},
"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": 0.249685,
"end_time": "2025-07-15T11:10:41.097705",
"message_sent": true,
"start_time": "2025-07-15T11:10:40.848020",
"timestamp": "2025-07-15T11:10:41.097705",
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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": 1128,
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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 \\\"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\\\",\\n \\\"Address AML screening concerns 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 strong compliance.\\\"\\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": 5.179122,
"end_time": "2025-07-15T11:14:03.230107",
"message_sent": true,
"start_time": "2025-07-15T11:13:58.050985",
"timestamp": "2025-07-15T11:14:03.230107",
"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 \"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\",\n \"Address AML screening concerns 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 strong compliance.\"\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 \\\"Prioritize resolving KYC compliance issues immediately to mitigate identity fraud risk.\\\",\\n \\\"Address AML screening concerns 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 strong compliance.\\\"\\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 \"biometric_verification\": 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": 372,
"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 \"biometric_verification\": 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 \\\"biometric_verification\\\": 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": 2.949249,
"end_time": "2025-07-15T11:07:33.167017",
"message_sent": true,
"start_time": "2025-07-15T11:07:30.217768",
"timestamp": "2025-07-15T11:07:33.167017",
"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 \"biometric_verification\": 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 \\\"biometric_verification\\\": 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 mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.",
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"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 mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\\n- **Credit History**: The history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot determine the length of credit history or any delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: \\n - Personal Loan: \\u20b945,000 with an EMI of \\u20b93,500 and a remaining tenure of 18 months.\\n - Auto Loan: \\u20b980,000 with an EMI of \\u20b94,200 and a remaining tenure of 24 months.\\n- **Total Outstanding**: \\u20b9125,000 across two loans.\\n- **Total Monthly EMI**: \\u20b97,700, which is manageable if the borrower\u0027s income supports it.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: \\n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\\n \\n### Risk Score Calculation\\nGiven the information provided, we can assign a risk score based on the following criteria:\\n- **CIBIL Score**: Unknown (high risk)\\n- **Repayment Track Record**: Excellent (low risk)\\n- **Loan Burden Ratio**: 28% (low risk)\\n- **Outstanding Loans**: Manageable (low risk)\\n\\n### Risk Score\\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\\n- Excellent repayment history and reasonable loan burden will lower the risk score.\\n\\n**Final Risk Score: 65/100**\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\\n\\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\", \"model_client_id\": \"risk_assessor\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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},
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"model": "gpt-4o-mini",
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"output": "To analyze the provided credit data and calculate the default risk, we will consider the various factors mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\n- **Credit History**: The history is also marked as \"UNRESOLVED_analyze_credit_history.\" Without specific details, we cannot determine the length of credit history or any delinquencies.\n\n### 2. Repayment Patterns\n- **Repayment Track Record**: The repayment track record is noted as \"excellent.\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\n\n### 3. Credit Utilization\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\n\n### 4. Existing Loan Burden\n- **Outstanding Loans**: \n - Personal Loan: \u20b945,000 with an EMI of \u20b93,500 and a remaining tenure of 18 months.\n - Auto Loan: \u20b980,000 with an EMI of \u20b94,200 and a remaining tenure of 24 months.\n- **Total Outstanding**: \u20b9125,000 across two loans.\n- **Total Monthly EMI**: \u20b97,700, which is manageable if the borrower\u0027s income supports it.\n\n### 5. Overall Risk Profile\n- **Risk Factors**: \n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\n \n### Risk Score Calculation\nGiven the information provided, we can assign a risk score based on the following criteria:\n- **CIBIL Score**: Unknown (high risk)\n- **Repayment Track Record**: Excellent (low risk)\n- **Loan Burden Ratio**: 28% (low risk)\n- **Outstanding Loans**: Manageable (low risk)\n\n### Risk Score\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\n- Excellent repayment history and reasonable loan burden will lower the risk score.\n\n**Final Risk Score: 65/100**\n\n### Recommendations\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\n\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\n__OUTPUTS__ {\"ai_response\": \"To analyze the provided credit data and calculate the default risk, we will consider the various factors mentioned: credit score and history, repayment patterns, credit utilization, existing loan burden, and overall risk profile.\\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 the score. A good CIBIL score typically ranges from 750 to 900, while a score below 600 is considered risky.\\n- **Credit History**: The history is also marked as \\\"UNRESOLVED_analyze_credit_history.\\\" Without specific details, we cannot determine the length of credit history or any delinquencies.\\n\\n### 2. Repayment Patterns\\n- **Repayment Track Record**: The repayment track record is noted as \\\"excellent.\\\" This indicates that the borrower has consistently made payments on time, which is a positive sign for creditworthiness.\\n\\n### 3. Credit Utilization\\n- **Loan Burden Ratio**: The loan burden ratio is 0.28 (28%), which is reasonable. A lower ratio indicates that the borrower is not over-leveraged and can manage their debts effectively.\\n- **Total Monthly EMI**: The total monthly EMI is 7700, which should be compared against the borrower\u0027s income to assess affordability.\\n\\n### 4. Existing Loan Burden\\n- **Outstanding Loans**: \\n - Personal Loan: \\u20b945,000 with an EMI of \\u20b93,500 and a remaining tenure of 18 months.\\n - Auto Loan: \\u20b980,000 with an EMI of \\u20b94,200 and a remaining tenure of 24 months.\\n- **Total Outstanding**: \\u20b9125,000 across two loans.\\n- **Total Monthly EMI**: \\u20b97,700, which is manageable if the borrower\u0027s income supports it.\\n\\n### 5. Overall Risk Profile\\n- **Risk Factors**: \\n - The lack of a CIBIL score and detailed credit history is a significant risk factor.\\n - However, the excellent repayment track record and reasonable loan burden ratio mitigate some risk.\\n \\n### Risk Score Calculation\\nGiven the information provided, we can assign a risk score based on the following criteria:\\n- **CIBIL Score**: Unknown (high risk)\\n- **Repayment Track Record**: Excellent (low risk)\\n- **Loan Burden Ratio**: 28% (low risk)\\n- **Outstanding Loans**: Manageable (low risk)\\n\\n### Risk Score\\n- Considering the unknown CIBIL score and credit history, we assign a higher risk score.\\n- Excellent repayment history and reasonable loan burden will lower the risk score.\\n\\n**Final Risk Score: 65/100**\\n\\n### Recommendations\\n1. **Resolve CIBIL Score**: Obtain and review the CIBIL score to get a complete picture of creditworthiness.\\n2. **Monitor Credit History**: Analyze the credit history for any missed payments or delinquencies that could affect future borrowing.\\n3. **Maintain Repayment Patterns**: Continue the excellent repayment habits to further improve creditworthiness.\\n4. **Manage Debt**: Keep the loan burden ratio below 30% and avoid taking on additional debt unless necessary.\\n5. **Increase Income**: If possible, increase income to ensure that the total EMI remains manageable relative to income.\\n\\nBy addressing these recommendations, the borrower can improve their credit profile and reduce the risk of default in the future.\", \"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": 1000
},
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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\": 1000}, \"missing_fields\": []}\n",
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"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\": 1000}, \"missing_fields\": []}\n",
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"final_underwriting_analysis": {
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"ai_response": "1. **Final Recommendation: DECLINE**\n\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.",
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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 Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation: DECLINE**\\n\\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\\n\\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\\n\\n4. **Reasoning for Decision:**\\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \\n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\\n\\n5. **Confidence Level: 95**\\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\", \"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 Declined)**\n\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\n\n4. **Reasoning for Decision:**\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\n\n5. **Confidence Level: 95**\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\n__OUTPUTS__ {\"ai_response\": \"1. **Final Recommendation: DECLINE**\\n\\n2. **Loan Terms and Conditions: N/A (Loan Declined)**\\n\\n3. **Risk Mitigation Measures: N/A (Loan Declined)**\\n\\n4. **Reasoning for Decision:**\\n - The application presents multiple unresolved issues across critical assessment areas, including application quality, document verification, income assessment, credit score calculation, default risk evaluation, compliance consolidation, and debt-to-income ratio calculation. \\n - Each of these factors is essential for making an informed lending decision. The lack of resolution in these areas indicates significant gaps in the applicant\u0027s financial profile and the overall integrity of the application.\\n - Without verified income, a calculated credit score, and a clear understanding of the applicant\u0027s debt-to-income ratio, it is impossible to assess the applicant\u0027s ability to repay the loan. Additionally, unresolved compliance issues raise concerns about regulatory adherence, which could expose the lending institution to legal risks.\\n - Given the high level of uncertainty and risk associated with this application, it is prudent to decline the loan request.\\n\\n5. **Confidence Level: 95**\\n - The decision is made with a high level of confidence due to the critical nature of the unresolved issues, which fundamentally undermine the viability of the loan application.\", \"model_client_id\": \"underwriting_analyst\", \"agent_type\": \"assistant\", \"model\": \"gpt-4o-mini\", \"provider\": \"openai\", \"status\": \"completed\"}",
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"generate_final_output": {
"error": "Python script failed with return code 1: Traceback (most recent call last):\n File \"/tmp/tmp0xx2mwz7.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/tmp0xx2mwz7.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"
},
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"output": "Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\n\n```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment status and confirmed probation.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"Permanent\",\n \"recommendation\": \"Continue monitoring employment status and ensure ongoing performance.\"\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Unable to calculate due to debt-to-income ratio script failure.\",\n \"debt_to_income_ratio\": \"Error in calculation\",\n \"recommendation\": \"Resolve the calculation error to assess debt servicing capability accurately.\"\n },\n \"financial_discipline\": {\n \"assessment\": \"Requires further analysis of bank statements and salary slips.\",\n \"bank_statement_analysis\": \"Pending\",\n \"salary_analysis\": \"Pending\",\n \"recommendation\": \"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\",\n \"identified_risks\": [\n \"Uncertainty in debt servicing capability\",\n \"Pending verification of income sources\",\n \"Dependence on a single income source\"\n ],\n \"recommendations\": [\n \"Resolve the debt-to-income calculation error.\",\n \"Collect and analyze salary slips and bank statements.\",\n \"Consider diversifying income sources to mitigate risk.\"\n ]\n },\n \"overall_income_adequacy_score\": {\n \"score\": 75,\n \"justification\": \"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\"\n }\n }\n}\n```\n\n### Explanation of the Assessment:\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\n \n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\n\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\n\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. Recommendations include resolving the calculation error and obtaining necessary financial documents.\n\n5. **Overall Income Adequacy Score**: A score of 75 reflects a generally stable income situation but highlights the need for further analysis to fully understand the individual\u0027s financial health.\n__OUTPUTS__ {\"ai_response\": \"Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\\n\\n```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"High stability due to permanent employment status and confirmed probation.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring employment status and ensure ongoing performance.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Unable to calculate due to debt-to-income ratio script failure.\\\",\\n \\\"debt_to_income_ratio\\\": \\\"Error in calculation\\\",\\n \\\"recommendation\\\": \\\"Resolve the calculation error to assess debt servicing capability accurately.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Requires further analysis of bank statements and salary slips.\\\",\\n \\\"bank_statement_analysis\\\": \\\"Pending\\\",\\n \\\"salary_analysis\\\": \\\"Pending\\\",\\n \\\"recommendation\\\": \\\"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\\\",\\n \\\"identified_risks\\\": [\\n \\\"Uncertainty in debt servicing capability\\\",\\n \\\"Pending verification of income sources\\\",\\n \\\"Dependence on a single income source\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Resolve the debt-to-income calculation error.\\\",\\n \\\"Collect and analyze salary slips and bank statements.\\\",\\n \\\"Consider diversifying income sources to mitigate risk.\\\"\\n ]\\n },\\n \\\"overall_income_adequacy_score\\\": {\\n \\\"score\\\": 75,\\n \\\"justification\\\": \\\"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\\\"\\n }\\n }\\n}\\n```\\n\\n### Explanation of the Assessment:\\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\\n \\n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\\n\\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\\n\\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. 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"output": "Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\n\n```json\n{\n \"income_assessment\": {\n \"income_stability\": {\n \"assessment\": \"High stability due to permanent employment status and confirmed probation.\",\n \"job_stability_score\": 88.5,\n \"employer_rating\": \"A+\",\n \"employment_type\": \"Permanent\",\n \"recommendation\": \"Continue monitoring employment status and ensure ongoing performance.\"\n },\n \"debt_servicing_capability\": {\n \"assessment\": \"Unable to calculate due to debt-to-income ratio script failure.\",\n \"debt_to_income_ratio\": \"Error in calculation\",\n \"recommendation\": \"Resolve the calculation error to assess debt servicing capability accurately.\"\n },\n \"financial_discipline\": {\n \"assessment\": \"Requires further analysis of bank statements and salary slips.\",\n \"bank_statement_analysis\": \"Pending\",\n \"salary_analysis\": \"Pending\",\n \"recommendation\": \"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\"\n },\n \"risk_factors\": {\n \"assessment\": \"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\",\n \"identified_risks\": [\n \"Uncertainty in debt servicing capability\",\n \"Pending verification of income sources\",\n \"Dependence on a single income source\"\n ],\n \"recommendations\": [\n \"Resolve the debt-to-income calculation error.\",\n \"Collect and analyze salary slips and bank statements.\",\n \"Consider diversifying income sources to mitigate risk.\"\n ]\n },\n \"overall_income_adequacy_score\": {\n \"score\": 75,\n \"justification\": \"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\"\n }\n }\n}\n```\n\n### Explanation of the Assessment:\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\n \n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\n\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\n\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. Recommendations include resolving the calculation error and obtaining necessary financial documents.\n\n5. **Overall Income Adequacy Score**: A score of 75 reflects a generally stable income situation but highlights the need for further analysis to fully understand the individual\u0027s financial health.\n__OUTPUTS__ {\"ai_response\": \"Based on the provided income data and the context of the analysis, here is a detailed JSON assessment:\\n\\n```json\\n{\\n \\\"income_assessment\\\": {\\n \\\"income_stability\\\": {\\n \\\"assessment\\\": \\\"High stability due to permanent employment status and confirmed probation.\\\",\\n \\\"job_stability_score\\\": 88.5,\\n \\\"employer_rating\\\": \\\"A+\\\",\\n \\\"employment_type\\\": \\\"Permanent\\\",\\n \\\"recommendation\\\": \\\"Continue monitoring employment status and ensure ongoing performance.\\\"\\n },\\n \\\"debt_servicing_capability\\\": {\\n \\\"assessment\\\": \\\"Unable to calculate due to debt-to-income ratio script failure.\\\",\\n \\\"debt_to_income_ratio\\\": \\\"Error in calculation\\\",\\n \\\"recommendation\\\": \\\"Resolve the calculation error to assess debt servicing capability accurately.\\\"\\n },\\n \\\"financial_discipline\\\": {\\n \\\"assessment\\\": \\\"Requires further analysis of bank statements and salary slips.\\\",\\n \\\"bank_statement_analysis\\\": \\\"Pending\\\",\\n \\\"salary_analysis\\\": \\\"Pending\\\",\\n \\\"recommendation\\\": \\\"Obtain and analyze bank statements and salary slips to evaluate spending habits and savings.\\\"\\n },\\n \\\"risk_factors\\\": {\\n \\\"assessment\\\": \\\"Potential risks include unresolved debt-to-income ratio and lack of detailed income documentation.\\\",\\n \\\"identified_risks\\\": [\\n \\\"Uncertainty in debt servicing capability\\\",\\n \\\"Pending verification of income sources\\\",\\n \\\"Dependence on a single income source\\\"\\n ],\\n \\\"recommendations\\\": [\\n \\\"Resolve the debt-to-income calculation error.\\\",\\n \\\"Collect and analyze salary slips and bank statements.\\\",\\n \\\"Consider diversifying income sources to mitigate risk.\\\"\\n ]\\n },\\n \\\"overall_income_adequacy_score\\\": {\\n \\\"score\\\": 75,\\n \\\"justification\\\": \\\"While the employment status is stable and the job is well-rated, the inability to assess debt servicing capability and financial discipline due to pending analyses lowers the score.\\\"\\n }\\n }\\n}\\n```\\n\\n### Explanation of the Assessment:\\n1. **Income Stability Assessment**: The individual has a stable job as a Senior Software Engineer with a high job stability score and a reputable employer, indicating a reliable income source.\\n \\n2. **Debt Servicing Capability**: The assessment is incomplete due to a failure in the debt-to-income calculation script, which needs to be resolved to provide a clear picture of the individual\u0027s ability to service debt.\\n\\n3. **Financial Discipline Evaluation**: Further analysis is required on bank statements and salary slips to evaluate the individual\u0027s financial habits and savings.\\n\\n4. **Risk Factors and Recommendations**: The main risks identified are the unresolved debt-to-income ratio and the lack of detailed income documentation. 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"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": 0.659779,
"end_time": "2025-07-15T11:07:33.278660",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.618881",
"timestamp": "2025-07-15T11:07:33.278660",
"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": 0.407876,
"end_time": "2025-07-15T11:07:29.442899",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.035023",
"timestamp": "2025-07-15T11:07:29.442899",
"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": 0.870913,
"end_time": "2025-07-15T11:07:29.998310",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.127397",
"timestamp": "2025-07-15T11:07:29.998310",
"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": 0.91421,
"end_time": "2025-07-15T11:07:33.240686",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.326476",
"timestamp": "2025-07-15T11:07:33.240686",
"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": 0.001428,
"ended_at": "2025-07-15T11:14:02.935617",
"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\": 0.001428, \"started_at\": \"2025-07-15T11:14:02.934189\", \"ended_at\": \"2025-07-15T11:14:02.935617\"}",
"return_code": 0,
"status": "completed",
"stderr": ""
},
"duration_seconds": 0.338626,
"end_time": "2025-07-15T11:14:03.162462",
"message_sent": true,
"start_time": "2025-07-15T11:14:02.823836",
"timestamp": "2025-07-15T11:14:03.162462",
"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\": 0.001428, \"started_at\": \"2025-07-15T11:14:02.934189\", \"ended_at\": \"2025-07-15T11:14:02.935617\"}",
"return_code": 0,
"route_reason": "no_conditions_matched",
"router_type": "condition",
"selected_route": "manual_review_path",
"started_at": "2025-07-15T11:14:02.934189",
"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": 0.350959,
"end_time": "2025-07-15T11:07:25.505496",
"message_sent": true,
"start_time": "2025-07-15T11:07:25.154537",
"timestamp": "2025-07-15T11:07:25.505496",
"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": 0.548361,
"end_time": "2025-07-15T11:07:29.630941",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.082580",
"timestamp": "2025-07-15T11:07:29.630941",
"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": 0.949851,
"end_time": "2025-07-15T11:07:30.125013",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.175162",
"timestamp": "2025-07-15T11:07:30.125013",
"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": 0.659779,
"end_time": "2025-07-15T11:07:33.278660",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.618881",
"timestamp": "2025-07-15T11:07:33.278660",
"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": 0.407876,
"end_time": "2025-07-15T11:07:29.442899",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.035023",
"timestamp": "2025-07-15T11:07:29.442899",
"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": 0.870913,
"end_time": "2025-07-15T11:07:29.998310",
"message_sent": true,
"start_time": "2025-07-15T11:07:29.127397",
"timestamp": "2025-07-15T11:07:29.998310",
"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": 0.91421,
"end_time": "2025-07-15T11:07:33.240686",
"message_sent": true,
"start_time": "2025-07-15T11:07:32.326476",
"timestamp": "2025-07-15T11:07:33.240686",
"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
}
}