How to Build a loan approval Agent Using AutoGen in Python for insurance

A loan approval agent for insurance evaluates borrower requests against underwriting rules, policy constraints, and document evidence, then produces a decision or a human-review recommendation. It matters because insurance lending is not just about credit risk; you also need compliance, auditability, and consistent treatment of applicants across jurisdictions.

Architecture

• •Applicant intake layer
  • •Normalizes structured inputs like income, debt, policy type, claim history, and jurisdiction.
• •Policy/rule engine
  • •Encodes underwriting thresholds, exclusions, and escalation rules.
• •AutoGen decision group
  • •Uses AssistantAgent instances to analyze the case from different angles: risk, compliance, and final decision.
• •Human review gate
  • •Routes borderline or high-risk cases to a UserProxyAgent or manual queue.
• •Audit logger
  • •Persists prompts, tool outputs, and final decisions for regulatory review.
• •Data access boundary
  • •Keeps PII, policy documents, and residency-sensitive data inside approved systems.

Implementation

1. •

   Create the agents and define the decision workflow

   AutoGen works well when you split responsibilities. One agent can reason about underwriting risk, another can check compliance language, and a user proxy can orchestrate execution and terminate the run.

   from autogen import AssistantAgent, UserProxyAgent
   
   llm_config = {
       "config_list": [
           {
               "model": "gpt-4o-mini",
               "api_key": "YOUR_OPENAI_API_KEY",
           }
       ],
       "temperature": 0,
   }
   
   risk_agent = AssistantAgent(
       name="risk_analyst",
       llm_config=llm_config,
       system_message=(
           "You assess loan applications for an insurance company. "
           "Focus on debt-to-income ratio, affordability, claim exposure, "
           "and whether the case should be approved or escalated."
       ),
   )
   
   compliance_agent = AssistantAgent(
       name="compliance_checker",
       llm_config=llm_config,
       system_message=(
           "You review loan applications for insurance compliance. "
           "Check for missing consent, jurisdiction issues, adverse action "
           "requirements, and auditability."
       ),
   )
   
   executor = UserProxyAgent(
       name="executor",
       human_input_mode="NEVER",
       max_consecutive_auto_reply=3,
       code_execution_config=False,
   )
   

2. •

   Pass a structured case summary into the conversation

   Keep the payload deterministic. In production, build this from your underwriting service or document pipeline before sending it to AutoGen.

   application = {
       "applicant_id": "A-10422",
       "jurisdiction": "US-NY",
       "loan_amount": 45000,
       "annual_income": 98000,
       "monthly_debt": 2100,
       "policy_type": "life_insurance_backed_loan",
       "claim_history_last_24m": 0,
       "consent_received": True,
   }
   
   prompt = f"""
   Review this insurance loan application:
   
   {application}
   
   Return:
   - risk assessment
   - compliance issues
   - final recommendation: approve / deny / escalate
   - short rationale suitable for audit logs
   """
   

3. •

   Run the multi-agent review and capture the result

   The simplest production pattern is to let one agent propose analysis and have another validate it before returning a final recommendation.

   def evaluate_application():
       chat_result = executor.initiate_chat(
           risk_agent,
           message=prompt,
           clear_history=True,
           max_turns=2,
       )
   
       # Second pass: compliance review of the same case summary
       compliance_result = executor.initiate_chat(
           compliance_agent,
           message=prompt,
           clear_history=True,
           max_turns=2,
       )
   
       return {
           "risk_analysis": chat_result.summary if hasattr(chat_result, "summary") else str(chat_result),
           "compliance_review": compliance_result.summary if hasattr(compliance_result, "summary") else str(compliance_result),
       }
   
   result = evaluate_application()
   print(result)
   

4. •

   Add a deterministic approval rule outside the model

   Do not let the model be the only decision-maker. Use it for reasoning; keep final policy enforcement in Python.

   def final_decision(app):
       dti = app["monthly_debt"] * 12 / app["annual_income"]
       
       if not app["consent_received"]:
           return "deny", "Missing consent"
       
       if app["jurisdiction"] == "US-NY" and app["loan_amount"] > 50000:
           return "escalate", "Jurisdictional threshold exceeded"
       
       if app["claim_history_last_24m"] > 1:
           return "escalate", "Recent claims history requires manual review"
       
       if dti > 0.45:
           return "deny", f"Debt-to-income too high: {dti:.2f}"
       
       return "approve", f"Within policy limits: DTI {dti:.2f}"
   
   decision, reason = final_decision(application)
   print(decision, reason)
   

Production Considerations

• •Keep PII out of prompts when possible
  • •Tokenize names, addresses, policy IDs, and claim references before sending data to the model.
• •Write full audit trails
  • •Store input payloads, agent messages, tool outputs, timestamps, model version, and final decision in immutable logs.
• •Enforce data residency
  • •If your insurance operation is region-bound, route inference through approved endpoints only and avoid cross-region logging.
• •Use guardrails for adverse actions
  • •Any denial or escalation should include a compliant rationale template reviewed by legal/compliance teams.

Common Pitfalls

• •Letting the LLM make the final credit decision
  • •Avoid this by keeping approval thresholds in Python or a rules engine. The model should explain; policy code should decide.
• •Sending raw customer documents into chat history
  • •Redact or extract only required fields first. Full PDFs and claims notes belong in controlled document systems.
• •Skipping human review for edge cases
  • •Borderline DTI values, missing consent, fraud indicators, or unusual jurisdictions should always route to manual underwriting.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.