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Challenge

Build an Insurance Fraud Detection Knowledge Graph Agent with LlamaIndex

Cross-policy insurance fraud claims involve complex networks of claimants, repair shops, and medical providers. Develop a LlamaIndex Property Graph RAG agent that identifies fraudulent claim rings and outputs graph-based evidence packages for adjusters.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Implement a LlamaIndex Property Graph RAG pipeline to uncover organized insurance claim fraud rings across linked entities.

Delivery guide

How work is evaluated

Evaluation

Evaluates ring detection precision on collusive insurance claim graph datasets.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Fraud Ring Threshold Test

Entities sharing >10 suspicious claims must trigger ring_detected == True.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Graph Ring Detection Recall

Percentage of true fraud rings identified. • target: 0.97 • range: 0.92-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Construct a LlamaIndex Property Graph Index from unstructured insurance loss claims.

  • Query multi-hop relationships between claimants, body shops, and medical clinics.

  • Detect collusive fraud rings with graph neural network heuristics and LLM synthesis.

  • Export structured evidence graphs for insurance fraud investigation teams.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic property graph dataset of connected motor and health insurance claims.

Challenge setup

Evaluates ring detection precision on collusive insurance claim graph datasets.

Challenge input

JSON containing claim_id, claimant_name, repair_shop_id, doctor_id, accident_location

LlamaIndex

Manages Property Graph construction and multi-hop graph retrieval.

Evaluated output

JSON with ring_detected (boolean), connected_claim_ids, evidence_graph_nodes

Evaluation checks
  • Entities sharing >10 suspicious claims must trigger ring_detected == True.
  • Percentage of true fraud rings identified. • target: 0.97 • range: 0.92-1
Configured evaluation evidence
  • Benchmark: InsuranceFraudGraphBench
  • Graph Ring Detection Recall target: 0.97
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • LlamaIndex · Optional
  • Llama 3.2 · Optional

Evaluation contract

  • Fraud Ring Threshold Test · Weight 1
  • Graph Ring Detection Recall · Weight 1

Metrics

  • Graph Ring Detection Recall · Target 0.97

Benchmarks

  • InsuranceFraudGraphBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing claim_id, claimant_name, repair_shop_id, doctor_id, accident_location

    Output: JSON with ring_detected (boolean), connected_claim_ids, evidence_graph_nodes

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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