AI Agents for payments: How to Automate claims processing (multi-agent with AutoGen)

Payments claims processing is where money, evidence, and deadlines collide. Chargebacks, refund disputes, failed transfers, and card network claims all require pulling data from PSPs, ledgers, KYC systems, support tickets, and policy rules before a decision can be made.

That workflow is still too manual in most payments teams. Multi-agent systems built with AutoGen can split the work into specialized roles: one agent gathers evidence, another checks policy and scheme rules, another drafts the case summary, and a final agent validates the decision before escalation or submission.

The Business Case

• •

  Cut claim handling time from 45-90 minutes to 10-20 minutes per case

  • •In a mid-market payments company handling 5,000 claims per month, that saves roughly 2,500-6,500 analyst hours annually.
  • •The biggest win is on first-pass triage: routing obvious refunds, duplicate disputes, and incomplete cases without human back-and-forth.
• •

  Reduce operating cost by 35-55% for manual review-heavy claims

  • •If your claims ops team costs $70k-$110k per FTE fully loaded, automating intake and evidence assembly can remove the need for 3-8 incremental hires as volume grows.
  • •This matters most for PSPs and fintechs with seasonal spikes after card-present outages or merchant onboarding issues.
• •

  Lower error rates in dispute packaging by 30-60%

  • •Common failures are missing timestamps, incorrect merchant descriptors, wrong reason codes, or incomplete evidence bundles.
  • •Multi-agent validation catches these before submission to networks like Visa or Mastercard, reducing avoidable chargeback losses and rework.
• •

  Improve SLA compliance from ~85% to 95%+

  • •Claims tied to card scheme deadlines or internal complaint SLAs get routed faster.
  • •That reduces regulatory exposure under consumer protection regimes and lowers escalation volume to support leaders.

Architecture

A production setup should be boring on purpose. Keep the agents narrow, the workflow deterministic where possible, and every decision traceable.

• •

  Agent orchestration layer: AutoGen + LangGraph

  • •Use AutoGen for multi-agent collaboration: intake agent, policy agent, evidence agent, and reviewer agent.
  • •Use LangGraph when you need explicit state transitions like received -> verified -> packaged -> approved -> escalated.
  • •This gives you control over retries, branching logic, and human-in-the-loop checkpoints.
• •

  Knowledge and retrieval layer: pgvector + Postgres

  • •Store policy docs, card network rules, SOPs, dispute playbooks, and prior resolved cases in Postgres with pgvector.
  • •Add metadata filters for jurisdiction, product line, merchant category code (MCC), claim type, and effective date.
  • •For regulated environments this is easier to audit than a loose document store.
• •

  Tooling layer: APIs into core payments systems

  • •Connect agents to your ledger service, transaction processor, CRM/ticketing system like Zendesk or Salesforce Service Cloud, KYC/AML records, and case management platform.
  • •The evidence agent should fetch:
    • •authorization logs
    • •settlement status
    • •chargeback reason code
    • •customer communications
    • •device/IP signals
    • •refund history
  • •Keep tool access read-only unless a human approves state changes.
• •

  Control plane: policy engine + observability

  • •Put hard rules in a policy engine such as Open Policy Agent (OPA) or a rules service.
  • •Log prompts, tool calls, outputs, confidence scores, and human overrides into your SIEM or observability stack.
  • •For model governance and auditability under SOC 2, GDPR access controls, and internal risk reviews, this is non-negotiable.

A practical pattern is:

1. •Intake agent classifies the claim.
2. •Evidence agent gathers facts from systems of record.
3. •Policy agent checks scheme rules and internal thresholds.
4. •Reviewer agent drafts the decision packet for an analyst or supervisor.

What Can Go Wrong

Getting Started

1. •

   Pick one claim type with clean economics

   • •Start with high-volume but low-complexity cases like duplicate card refunds or failed ACH/instant payment reversals.
   • •Avoid complex fraud disputes on day one.
   • •Target a pilot scope of 1 payment rail, 1 region, and 1 operations team of 3-5 analysts.
2. •

   Map the current workflow end to end

   • •Document every input needed to resolve the claim:
     • •transaction ID
     • •settlement date
     • •scheme reason code
     • •merchant response window
     • •customer communication history
   • •Measure baseline metrics for two weeks:
     • •average handling time
     • •first-pass resolution rate
     • •rework rate
     • •SLA breach rate
3. •

   Build the multi-agent MVP in 4-6 weeks

   • •Week 1-2: connect tools and build retrieval over policies using pgvector.
   • •Week 3-4: implement AutoGen agents for intake/evidence/policy/review.
   • •Week 5-6: add guardrails with OPA-style rules plus human approval for edge cases.
   • •Keep one engineer focused on orchestration/data plumbing, one backend engineer on integrations, one ML engineer on prompts/evals, and one ops lead from claims/compliance.
4. •

   Run shadow mode before auto-action

   • •For at least 30 days, have the agents produce decisions without sending them downstream automatically.
   • •Compare against analyst outcomes and measure precision by claim type.
   • •Only then allow auto-resolution for narrow categories where accuracy stays above your target threshold.

If you do this right, AI agents won’t replace claims operations overnight. They will turn claims processing into a controlled system: faster intake، cleaner evidence packs، fewer manual errors، and better compliance posture without giving up auditability.

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.