How to Build a claims processing Agent Using LlamaIndex in Python for wealth management

A claims processing agent in wealth management takes a client request, extracts the relevant facts from documents and messages, checks policy and account context, and drafts a decision or next action for a human reviewer. It matters because claims in this domain are not just operational tickets; they affect client trust, regulatory exposure, and the audit trail around who approved what and why.

Architecture

• •

  Document ingestion layer

  • •Pulls PDFs, emails, scanned forms, and CRM notes into a normalized index.
  • •In wealth management, this usually includes KYC docs, account statements, beneficiary forms, and claim letters.
• •

  Retrieval layer

  • •Uses VectorStoreIndex to fetch the most relevant policy clauses, prior cases, and account metadata.
  • •This keeps the agent grounded in firm-approved source material.
• •

  Claim extraction layer

  • •Converts unstructured text into structured fields like claimant name, account number, event date, amount requested, and supporting evidence.
• •

  Decision support layer

  • •Applies business rules and policy constraints before generating a recommendation.
  • •For regulated workflows, this should produce “approve / reject / needs human review,” not free-form answers.
• •

  Audit and traceability layer

  • •Stores retrieved chunks, model outputs, timestamps, and source document IDs.
  • •You need this for compliance review, dispute handling, and internal model governance.
• •

  Human-in-the-loop handoff

  • •Routes low-confidence or high-risk claims to an operations analyst.
  • •This is mandatory when the claim touches suitability, fiduciary obligations, or jurisdiction-specific rules.

Implementation

1) Build an index over policy documents and claim artifacts

Start with a document loader plus a vector index. For wealth management claims processing, keep policy docs separate from client files so you can enforce access control later.

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex

# Load approved policy docs and sample claim files
policy_docs = SimpleDirectoryReader("./data/policies").load_data()
claim_docs = SimpleDirectoryReader("./data/claims").load_data()

# Build separate indexes if you need stricter access boundaries
policy_index = VectorStoreIndex.from_documents(policy_docs)
claim_index = VectorStoreIndex.from_documents(claim_docs)

policy_retriever = policy_index.as_retriever(similarity_top_k=3)
claim_retriever = claim_index.as_retriever(similarity_top_k=3)

2) Create a structured extraction prompt with LlamaIndex

Use LlamaIndex’s query engine to extract claim fields from the incoming request. The key pattern is to force structure early so downstream logic does not parse prose.

from llama_index.core import PromptTemplate

extract_prompt = PromptTemplate(
    """
You are processing a wealth management claim.

Extract the following fields as JSON:
- claimant_name
- account_id
- claim_type
- event_date
- requested_amount
- supporting_documents
- urgency_level

Text:
{context_str}

Return only valid JSON.
"""
)

claim_query_engine = claim_index.as_query_engine(
    text_qa_template=extract_prompt,
    similarity_top_k=3,
)

response = claim_query_engine.query(
    "Client says their portfolio transfer failed after the custodian change."
)

print(response)

3) Ground the decision in policy retrieval

Now retrieve the relevant policy clauses and combine them with the extracted claim. In production you would pass both into your own decision function or workflow step.

def get_policy_context(query: str) -> str:
    nodes = policy_retriever.retrieve(query)
    return "\n\n".join([node.node.get_content() for node in nodes])

claim_text = """
Client reports a failed portfolio transfer after custodian migration.
Requested reimbursement: $12,500. Account ID: WM-88421.
Supporting docs include transfer confirmation and email chain.
"""

policy_context = get_policy_context(claim_text)

decision_prompt = f"""
You are an internal claims reviewer for a wealth management firm.

Policy context:
{policy_context}

Claim text:
{claim_text}

Decide one of:
- APPROVE
- REJECT
- HUMAN_REVIEW

Return:
1. decision
2. reason
3. missing_information
4. audit_notes
"""

decision_engine = claim_index.as_query_engine()
decision_response = decision_engine.query(decision_prompt)

print(decision_response)

4) Add an agent wrapper for tool use

If you want the model to retrieve both claims data and policy data dynamically, wrap retrievers as tools with QueryEngineTool and let an OpenAIAgent orchestrate them.

from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.agent import OpenAIAgent

policy_tool = QueryEngineTool(
    query_engine=policy_index.as_query_engine(),
    metadata=ToolMetadata(
        name="policy_lookup",
        description="Lookup internal claims policies and approval thresholds."
    ),
)

claim_tool = QueryEngineTool(
    query_engine=claim_index.as_query_engine(),
    metadata=ToolMetadata(
        name="claim_lookup",
        description="Lookup historical claim records and supporting evidence."
    ),
)

agent = OpenAIAgent.from_tools(
    tools=[policy_tool, claim_tool],
    system_prompt=(
        "You process wealth management claims. "
        "Always cite retrieved sources. "
        "Escalate anything ambiguous or high-risk to HUMAN_REVIEW."
    ),
)

result = agent.chat(
    "Review claim WM-88421 for reimbursement eligibility under transfer failure policy."
)

print(result)

Production Considerations

• •

  Compliance controls

  • •Log every retrieved node ID, prompt version, model response, and final disposition.
  • •Keep immutable audit records so compliance can reconstruct decisions during reviews.
• •

  Data residency

  • •Keep client data inside approved regions.
  • •If your firm operates across jurisdictions, separate indexes by region rather than mixing EU/US client artifacts in one store.
• •

  Guardrails

  • •Block the agent from making final decisions on high-value claims without analyst approval.
  • •Add threshold-based escalation for missing documents, sanction hits, unusual amounts, or conflicting account ownership.
• •

  Monitoring

  • •Track retrieval quality, escalation rate, hallucination incidents, and turnaround time.
  • •In wealth management you also want drift alerts when policy language changes or new product types appear.

Common Pitfalls

• •

  Mixing client data with policy content in one index

  • •This creates access-control problems fast.
  • •Keep separate indexes or namespaces for policies, client records, and historical cases.
• •

  Letting the model produce open-ended recommendations

  • •“Looks fine” is not an operational output.
  • •Force structured outputs like APPROVE, REJECT, or HUMAN_REVIEW, then route accordingly.
• •

  Skipping source citation

  • •If an analyst cannot see which clause drove the recommendation, your audit trail is weak.
  • •Always store retrieved chunks alongside the final answer.

A good claims processing agent in wealth management does not replace operations staff. It reduces manual reading time while preserving controls around compliance, auditability, and jurisdictional data handling.

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.