How to Build a transaction monitoring Agent Using AutoGen in Python for insurance

A transaction monitoring agent for insurance watches claims, premium payments, policy changes, and beneficiary updates for suspicious patterns. It matters because fraud, identity manipulation, and laundering often show up as small anomalies across many transactions before they become losses or regulatory problems.

Architecture

• •Event ingestion layer
  • •Pulls claim events, payment events, policy admin events, and customer profile changes from queues or APIs.
• •Rules engine
  • •Applies deterministic checks first: velocity spikes, duplicate bank accounts, round-dollar claims, rapid beneficiary changes.
• •AutoGen investigation agent
  • •Uses AssistantAgent to review the event context, explain risk signals, and decide whether to escalate.
• •Tool layer
  • •Exposes functions for case lookup, sanctions screening, customer history retrieval, and audit logging.
• •Case management sink
  • •Writes structured outcomes to a case system or SIEM with reason codes and evidence.
• •Human review queue
  • •Routes high-risk or ambiguous cases to an analyst for final disposition.

Implementation

1) Install AutoGen and define the data model

Use AutoGen’s agent framework directly. For a production service, keep the transaction payload structured so the model sees stable fields instead of raw text blobs.

pip install pyautogen pydantic

from pydantic import BaseModel
from typing import Literal, Optional

class TransactionEvent(BaseModel):
    event_id: str
    policy_id: str
    customer_id: str
    event_type: Literal["claim", "premium_payment", "policy_change", "beneficiary_update"]
    amount: float
    currency: str = "USD"
    channel: str
    country: str
    timestamp: str
    notes: Optional[str] = None

2) Build tools the agent can call

AutoGen works well when you keep the model on a short leash and expose only approved functions. These tools should be deterministic and auditable.

from typing import Dict, Any

def get_customer_history(customer_id: str) -> Dict[str, Any]:
    # Replace with real DB/API access.
    return {
        "customer_id": customer_id,
        "claims_last_30d": 4,
        "premium_payment_failures_last_90d": 2,
        "beneficiary_changes_last_60d": 3,
        "risk_flag": True,
    }

def write_audit_log(event_id: str, decision: str, reason: str) -> str:
    # Replace with SIEM / audit store write.
    return f"audited:{event_id}:{decision}"

def create_case(event_id: str, severity: str, summary: str) -> Dict[str, Any]:
    # Replace with case management API call.
    return {"case_id": f"CASE-{event_id}", "severity": severity, "summary": summary}

3) Create the AutoGen agent and run the investigation loop

This pattern uses AssistantAgent plus tool registration. The agent receives one event at a time, inspects history through tools, and returns a structured recommendation.

import autogen

llm_config = {
    "config_list": [
        {
            "model": "gpt-4o-mini",
            "api_key": os.environ["OPENAI_API_KEY"],
        }
    ],
    "temperature": 0,
}

assistant = autogen.AssistantAgent(
    name="insurance_txn_monitor",
    llm_config=llm_config,
    system_message=(
        "You are an insurance transaction monitoring analyst. "
        "Assess fraud/AML risk using only provided data and approved tools. "
        "Return a JSON-like recommendation with decision, severity, reasons."
    ),
)

# Register callable tools on the assistant side.
assistant.register_function(
    function_map={
        "get_customer_history": get_customer_history,
        "write_audit_log": write_audit_log,
        "create_case": create_case,
    }
)

def investigate(event):
    prompt = f"""
Review this insurance transaction event:

{event.model_dump_json(indent=2)}

Use get_customer_history(customer_id) if needed.
If risk is high, call create_case(event_id, severity, summary).
Always call write_audit_log(event_id, decision, reason).

Return:
decision = 'clear' | 'review' | 'escalate'
severity = 'low' | 'medium' | 'high'
reason = short explanation
"""
    result = assistant.generate_reply(messages=[{"role": "user", "content": prompt}])
    return result

if __name__ == "__main__":
    event = TransactionEvent(
        event_id="evt-10021",
        policy_id="pol-7781",
        customer_id="cust-442",
        event_type="beneficiary_update",
        amount=0.0,
        channel="web",
        country="US",
        timestamp="2026-04-21T10:15:00Z",
        notes="Third beneficiary change in two months after recent claim payout.",
    )

    print(investigate(event))

4) Add a deterministic pre-filter before the LLM

Do not send every event straight to the model. In insurance workflows, simple rules catch most noisy traffic and reduce cost while improving auditability.

def rule_score(event: TransactionEvent) -> int:
    score = 0
    if event.event_type == "beneficiary_update":
        score += 30
    if event.amount >= 10000:
        score += 20
    if event.country not in {"US", "CA", "GB"}:
        score += 15
    if event.notes and "urgent" in event.notes.lower():
        score += 10
    return score

def monitor(event: TransactionEvent):
    score = rule_score(event)
    if score < 20:
        write_audit_log(event.event_id, "clear", f"rule_score={score}")
        return {"decision": "clear", "severity": "low", "reason": f"rule_score={score}"}

    return investigate(event)

Production Considerations

• •Audit every decision
  • •Store input payloads, tool calls, model output, and final disposition. Insurance auditors will ask why an alert was raised or suppressed.
• •Enforce data residency
  • •Keep PII and claim data in-region. If your insurer operates in multiple jurisdictions, route EU policyholder data to EU-hosted inference and storage.
• •Use strict guardrails
  • •Limit tools to read-only lookups unless escalation is required. Never let the agent directly modify policy records or payouts.
• •Monitor false positives by line of business
  • •Fraud patterns differ between life, health, auto, and property products. Track precision/recall per product line instead of one global metric.

Common Pitfalls

• •Sending raw unfiltered data to the model
  • •This increases noise and leaks unnecessary PII. Pre-filter with rules and redact fields like SSNs or full bank details before prompting.
• •Letting the agent make final decisions without human review
  • •In insurance operations you need explainability and escalation paths. Use the agent for triage; keep adjudication with analysts for high-severity cases.
• •Skipping structured outputs
  • •Free-form text is hard to audit downstream. Force a fixed schema for decision, severity, reason, and case IDs so your pipeline can route alerts reliably.

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.