How to Build a claims processing Agent Using LlamaIndex in Python for investment banking

A claims processing agent in investment banking ingests claim documents, extracts the relevant facts, checks them against policy and trade records, and drafts a decision package for human review. It matters because claims are expensive when they sit in queues, get handled inconsistently, or miss compliance checks around auditability, data residency, and approval thresholds.

Architecture

• •

  Document ingestion layer

  • •Pulls in PDFs, emails, scans, and structured claim forms from approved internal sources.
  • •Normalizes them into text plus metadata like claim ID, desk, jurisdiction, and retention class.
• •

  Indexing layer

  • •Uses VectorStoreIndex for semantic retrieval over claim histories, policy docs, and prior adjudications.
  • •Keeps source chunks small enough to cite precisely during review.
• •

  Retrieval and reasoning layer

  • •Uses QueryEngine or a RetrieverQueryEngine to answer questions like “Is this claim eligible under desk policy?”
  • •Grounds responses in internal policy and transaction evidence.
• •

  Decision support layer

  • •Produces a structured output: approve, reject, escalate, or request more info.
  • •Keeps the model out of final authority; humans sign off on exceptions.
• •

  Audit and traceability layer

  • •Stores retrieved nodes, prompts, outputs, timestamps, and reviewer actions.
  • •Supports post-trade review, compliance audits, and dispute resolution.
• •

  Guardrails layer

  • •Redacts sensitive fields before model calls.
  • •Enforces jurisdictional routing so regulated data stays in approved regions.

Implementation

1) Load claim documents with metadata

For investment banking use cases, metadata is not optional. You need claim source, business unit, jurisdiction, and retention tags so downstream retrieval can filter correctly.

from llama_index.core import Document

claim_docs = [
    Document(
        text=(
            "Claim ID C-1042: Client alleges settlement delay caused financing loss "
            "on equity swap execution dated 2024-08-14. Supporting emails attached. "
            "Requested compensation: $180,000."
        ),
        metadata={
            "claim_id": "C-1042",
            "desk": "Equities",
            "jurisdiction": "UK",
            "source": "email",
            "retention_class": "claims"
        },
    ),
    Document(
        text=(
            "Policy excerpt: Claims above $100,000 require escalation to Legal "
            "and Compliance. Settlement delays due to market-wide outages may be excluded."
        ),
        metadata={
            "policy_id": "POL-17",
            "jurisdiction": "UK",
            "source": "policy"
        },
    ),
]

2) Build a vector index over the approved corpus

Use VectorStoreIndex.from_documents() to create the retrieval layer. In production you would back this with a governed vector store; the API stays the same.

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(claim_docs)
query_engine = index.as_query_engine(similarity_top_k=3)

3) Ask for a structured claims assessment

The agent should return a decision summary that an analyst can review. Keep the prompt narrow and force it to cite evidence from retrieved context.

response = query_engine.query(
    """
    Assess whether claim C-1042 should be approved automatically,
    escalated to Legal/Compliance, or rejected.
    
    Return:
    - decision
    - rationale
    - cited evidence
    - missing information
    """
)

print(response)

If you need stricter control over formatting, use LlamaIndex’s response synthesis components with a custom prompt template. The important part is that the answer is grounded in retrieved nodes rather than free-form generation.

4) Add an explicit retriever for audit-friendly workflows

For claims handling you usually want the raw retrieved nodes as well as the final answer. That gives you a clean audit trail showing what evidence was used.

retriever = index.as_retriever(similarity_top_k=3)
nodes = retriever.retrieve("Claim C-1042 eligibility under UK policy")

for node in nodes:
    print("SCORE:", node.score)
    print("TEXT:", node.node.get_text())
    print("METADATA:", node.node.metadata)
    print("-" * 80)

That pattern is useful when you need to persist evidence into your case management system. Store the retrieved node IDs alongside the final recommendation so compliance can reconstruct every decision later.

Production Considerations

• •

  Deploy inside your controlled environment

  • •Keep indexing and inference inside approved VPCs or private cloud regions.
  • •For regulated desks, enforce data residency by routing UK/EU claims to regional infrastructure only.
• •

  Log every decision path

  • •Persist input document hashes, retrieved node IDs, prompts, model version, output text, and human reviewer actions.
  • •This is what makes the agent defensible during internal audit or regulatory review.
• •

  Add guardrails before model calls

  • •Redact account numbers, client names where required by policy, and any restricted trading data.
  • •Block generation if the claim references prohibited content such as MNPI or unresolved surveillance cases.
• •

  Use human-in-the-loop escalation

  • •Auto-process only low-risk claims with clear policy matches.
  • •Escalate anything above threshold value, cross-border disputes, legal exceptions, or ambiguous evidence.

Common Pitfalls

1. •

   Treating retrieval as optional

   • •If the model answers from memory instead of indexed policy and evidence docs, you will get inconsistent outcomes.
   • •Fix it by forcing all decisions through VectorStoreIndex retrieval and storing cited nodes with each case.
2. •

   Ignoring metadata filters

   • •Mixing jurisdictions or desks in one search space creates bad recommendations and compliance risk.
   • •Fix it by tagging every document with jurisdiction, desk, source type, and retention class at ingestion time.
3. •

   Letting the agent make final decisions on high-value claims

   • •In investment banking this becomes a governance problem fast.
   • •Fix it by limiting automation to triage and draft recommendations; keep approval authority with Legal, Compliance, or operations managers.
4. •

   Skipping audit storage

   • •If you cannot explain why a claim was escalated or rejected six months later, you do not have a production system.
   • •Fix it by persisting prompts, outputs, retrieval results, model versioning, and reviewer sign-off in immutable storage.

Keep learning

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• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.