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

Investment banking operations still run on a lot of manual exception handling: trade breaks, settlement claims, fee disputes, failed confirmations, and client reimbursement requests. Those workflows are document-heavy, policy-driven, and slow because analysts have to read emails, compare systems of record, and route cases to the right desk. Multi-agent AI with AutoGen fits here because you can split the work into specialist agents that triage, validate, investigate, and draft responses without turning the whole process into one brittle prompt.

The Business Case

• •

  Cut case handling time by 40-65%

  • •A claims analyst who spends 20-30 minutes per case on intake, evidence gathering, and routing can get that down to 8-12 minutes for standard cases.
  • •In a desk handling 5,000 claims or disputes per month, that is roughly 1,000-2,000 analyst hours saved monthly.
• •

  Reduce operational cost by 25-40%

  • •For a middle-office or operations team with 15-30 FTEs supporting claims and exceptions, automation can remove a large chunk of repetitive review work.
  • •That translates into $300K-$900K annual savings depending on geography, labor mix, and whether you use the model for straight-through processing or just assisted review.
• •

  Lower error rates by 30-50%

  • •Most manual mistakes come from missed attachments, wrong product classification, duplicate claim creation, and inconsistent policy application.
  • •An agentic workflow with validation steps can reduce those errors from around 3-5% down to 1-2% on standardized claim types.
• •

  Improve SLA performance

  • •If your current average resolution time is 2-5 business days for non-complex claims, an agent pipeline can bring first-response time down to under 10 minutes and resolution for routine cases to same day.
  • •That matters when you are dealing with client-facing desks where delayed settlement claims become escalation risk.

Architecture

A production setup should not be one monolithic chatbot. Use a multi-agent system with hard boundaries between extraction, reasoning, compliance checks, and human approval.

• •

  Ingestion and normalization layer

  • •Pull data from email, PDF attachments, CRM/ticketing systems like ServiceNow or Salesforce, and internal reference data from the OMS/EMS or data warehouse.
  • •Use OCR plus document parsing for scanned forms and statements.
  • •Store normalized case artifacts in PostgreSQL; use pgvector for semantic retrieval over policies, prior claims, ISDA templates, settlement rules, and desk playbooks.
• •

  Agent orchestration layer

  • •Use AutoGen as the multi-agent coordinator.
  • •Typical agents:
    • •Intake Agent: classifies claim type and extracts parties, dates, amounts, instrument identifiers.
    • •Evidence Agent: retrieves supporting records from internal systems and compares them against the claim.
    • •Policy Agent: checks eligibility against internal controls and regulatory rules.
    • •Drafting Agent: prepares analyst notes or client response drafts.
    • •Escalation Agent: routes exceptions to legal/compliance/operations.
  • •If you want stricter control flow than free-form conversation loops, wrap AutoGen inside LangGraph so every state transition is explicit.
• •

  Decision support and guardrails

  • •Use deterministic rules for thresholds: amount limits, product exclusions, counterparty risk flags, aging buckets.
  • •Keep LLM output constrained to structured JSON with schema validation.
  • •Add a policy engine for approvals so no agent can finalize anything above a defined materiality threshold.
• •

  Auditability and monitoring

  • •Log every retrieved document chunk, prompt version, model version, tool call, decision branch, and human override.
  • •Push traces into your observability stack; use OpenTelemetry-compatible logging if possible.
  • •For regulated environments targeting SOC 2 controls or internal model risk governance under Basel III-style expectations around operational resilience and control effectiveness, audit trails are not optional.

What Can Go Wrong

• •

  Regulatory risk

  • •Claims often touch customer data or confidential counterparty information. If personal data is involved across regions like the EU or UK, GDPR applies; if healthcare-related benefit claims ever enter the workflow in adjacent businesses like employee benefits administration there may be HIPAA implications too.
  • •Mitigation:
    • •Redact sensitive fields before model calls where possible.
    • •Keep data residency aligned with jurisdictional requirements.
    • •Use approved models only; no shadow IT endpoints.
    • •Maintain immutable logs for audit review.
• •

  Reputation risk

  • •A bad automated response to a prime brokerage dispute or failed trade claim can damage client trust fast. One incorrect denial letter is enough to create escalation at the MD level.
  • •Mitigation:
    • •Never let the draft go directly to clients without human approval in pilot phase.
    • •Set confidence thresholds so low-confidence cases always route to an analyst.
    • •Build response templates reviewed by legal/compliance before launch.
• •

  Operational risk

  • •Agents can hallucinate missing evidence or misread stale records if your source systems are inconsistent. In investment banking that becomes expensive when claims involve settlement windows or contractual deadlines.
  • •Mitigation:
    • •Require source citations in every decision summary.
    • •Use dual verification: one agent extracts facts; another independently validates them against system records.
    • •Start with narrow claim types like fee disputes or standard settlement fails before expanding into complex derivatives or cross-border cases.

Getting Started

1. •

   Pick one narrow workflow

   • •Start with a high-volume but low-complexity process such as failed settlement claims under a defined threshold or fee dispute intake.
   • •Avoid complex structured products on day one.
2. •

   Build a pilot team of 5-7 people

   • •You need:
     • •1 product owner from operations
     • •1 compliance lead
     • •1 engineering lead
     • •1 data engineer
     • •1 ML/agent engineer
     • •1 QA/test analyst
     • •optional SME from legal or client services
   • •This is enough to ship a pilot in 8-12 weeks if your data access is already approved.
3. •

   Instrument the workflow before adding autonomy

   • •Capture baseline metrics first:
     • •average handling time
     • •first-response SLA
     • •exception rate
     • •human override rate
   • •Then compare agent-assisted performance against that baseline. If you cannot measure uplift cleanly during pilot mode under SOC 2-style change control discipline in production-like environments then you do not have an implementation plan yet.
4. •

   Move from assistive to semi-autonomous

   • •Phase 1: agent drafts summaries and recommended actions
   • •Phase 2: agent auto-triages standard cases below threshold
   • •Phase 3: agent closes routine cases with human spot-checking
   • •Keep compliance sign-off at each stage

If you are running this inside an investment bank with real client exposure, do not start by asking whether agents can “replace ops.” Start by asking which claim types are repetitive enough to automate safely while preserving auditability. That is where AutoGen earns its place: not as a demo layer, but as an operating model for controlled automation.

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.