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

Opening

Claims processing in investment banking is usually buried inside exception handling, trade breaks, settlement disputes, margin calls, and client reimbursement workflows. The pain is the same: analysts spend hours reading PDFs, emails, tickets, and supporting evidence, then manually routing cases across ops, legal, compliance, and finance.

A single-agent AutoGen setup fits here because the workflow is mostly sequential: ingest the claim, extract facts, validate policy and counterparty data, draft a recommendation, and hand off for human approval. You are not replacing the control function; you are removing the repetitive document triage that slows down resolution.

The Business Case

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  Reduce average handling time by 40-60%

  • •A claims analyst who currently spends 45-90 minutes per case on intake, classification, evidence review, and drafting can get cut to 15-30 minutes with an agent that pre-populates the case file.
  • •In a desk handling 2,000 claims per month, that is roughly 1,500-2,500 analyst hours saved monthly.
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  Lower operational cost by 20-35%

  • •For a mid-sized investment bank ops team with 8-15 FTEs in claims and exceptions management, automation can remove enough manual work to avoid 2-4 incremental hires.
  • •At fully loaded costs of $140k-$220k per FTE, that is real annual savings.
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  Reduce error rates from 3-5% to below 1%

  • •Manual claims processing tends to fail on missed attachments, incorrect counterparty mapping, wrong booking references, or stale policy versions.
  • •An agent with validation rules can enforce required fields and reduce rework caused by incomplete submissions.
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  Improve SLA compliance by 25-40%

  • •If your current median resolution time is 3-5 business days for standard claims and longer for complex exceptions, an agent can cut triage latency to minutes.
  • •That matters when your internal SLA is tied to client escalation risk and desk-level P&L impact.

Architecture

A production single-agent design should be boring in the right ways. Keep it narrow, auditable, and easy to shut off.

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  Agent orchestration layer: AutoGen

  • •Use one primary agent for intake and decision support.
  • •Keep tool use explicit: document retrieval, policy lookup, booking-system query, case creation.
  • •Avoid multi-agent chatter unless you have a strong reason; it adds latency and makes audit trails harder.
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  Workflow and guardrails: LangGraph

  • •Model the process as a state machine: received -> extracted -> validated -> drafted -> human_review -> closed.
  • •Add hard stops for missing KYC data, sanction hits, or unresolved counterparty mismatches.
  • •Use deterministic transitions for regulated steps instead of letting the model improvise.
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  Retrieval layer: pgvector + PostgreSQL

  • •Store policy docs, SOPs, prior claim summaries, product terms, and exception playbooks in Postgres with pgvector.
  • •Retrieve only approved internal sources so the agent does not hallucinate policy language.
  • •Partition data by desk or legal entity if your operating model requires ring-fencing.
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  Integration layer: service APIs + case management

  • •Connect to internal systems like ServiceNow, Jira Service Management, or a custom claims platform.
  • •Pull from source systems such as trade capture platforms, document stores, email archives, and reference data services.
  • •Log every action into an immutable audit table with timestamped prompts, retrieved documents, tool calls, and final recommendations.

A practical stack looks like this:

What Can Go Wrong

Regulatory risk

Claims often touch client data, transaction records, AML flags, or personal information. If you process EU client data without proper controls under GDPR, or expose sensitive records through weak access control even if you are SOC 2 certified internally only on paper access controls are not enough.

Mitigation:

• •Mask PII before retrieval where possible.
• •Restrict the agent to approved datasets with row-level security.
• •Maintain full prompt/tool-call audit logs.
• •Run privacy reviews with Legal and Compliance before pilot launch.
• •If healthcare-related claim data ever enters scope through employee benefits or insurance-linked workflows in a bank-owned entity set treat HIPAA as relevant too.

Reputation risk

A bad recommendation on a high-value dispute can damage client trust fast. In investment banking clients do not care that “the model said so”; they care that their margin call was mishandled or their settlement claim was rejected without basis.

Mitigation:

• •Require human approval for all adverse decisions.
• •Use confidence thresholds and escalate low-confidence cases automatically.
• •Keep response templates conservative and cite source documents explicitly.
• •Start with low-risk claims such as document completeness checks before touching financial determinations.

Operational risk

If the agent pulls stale policy text or misreads a booking reference it can create downstream breaks across finance ops reconciliation and client service. That becomes expensive when claims feed into capital reporting or reserve calculations under frameworks influenced by Basel III controls.

Mitigation:

• •Version every policy document used by retrieval.
• •Add validation against golden reference data for account IDs trade IDs LEIs and booking dates.
• •Put rate limits on tool calls to prevent runaway loops.
• •Build a kill switch so operations can disable automation during incident response.

Getting Started

Step 1: Pick one narrow claim type

Start with a workflow that has clear inputs and low ambiguity:

• •failed settlement reimbursement requests
• •fee dispute intake
• •missing-document exceptions
• •standard client complaint triage

Do not start with complex litigation-adjacent cases or anything requiring discretionary legal judgment. Pick one desk one region one legal entity. That keeps scope manageable.

Step 2: Build the control plane first

Before any model tuning:

• •define approval thresholds
• •define escalation rules
• •map source systems
• •define audit fields
• •set retention policies

This usually takes 2-3 weeks with a team of:

• •1 product owner from operations
• •1 backend engineer
• •1 ML/AI engineer
• •1 compliance partner part-time The goal is not speed. The goal is proving you can govern the workflow.

Step 3: Run a shadow pilot for 4 weeks

Feed live cases into the agent but do not let it make final decisions. Compare its output against analyst outcomes on:

• •extraction accuracy
• •classification accuracy
• •time-to-triage
• •escalation precision
• •false positive rate on policy breaches

Target at least 200–500 cases in the pilot if volume allows. That gives you enough signal to see where the process breaks.

Step 4: Move to assisted production

Once accuracy is stable:

• •let the agent draft case summaries 0 recommend next actions 0 pre-fill system fields 0 route edge cases to humans

Keep humans in the loop for approvals until you have six to eight weeks of clean production metrics. After that you can expand horizontally into adjacent claim types or another booking center.

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.