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Challenge

Build an Automated Insurtech Claims Triaging System with OpenAI Agents SDK

Insurance providers handle high volumes of health and casualty claims requiring fast, accurate triaging and fraud scoring. Build an autonomous claims processing agent using the OpenAI Agents SDK to parse intake documents, run automated policy validation, and route clean claims directly to payout.

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Challenge brief

What you are building

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

Implement an autonomous claims triaging system with OpenAI Agents SDK to parse insurance documentation and assign automated payout decisions.

Delivery guide

How work is evaluated

Evaluation

Evaluates OpenAI Agents SDK agent accuracy in triaging claims, identifying fraudulent billing indicators, and deciding settlement routing.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

approval_logic_pass

Ensures low-risk claim within policy limit is set to AUTO_APPROVED

Binary check

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

Dimension 2

fraud_score_accuracy

Precision of fraud risk rating against synthetic evaluation baseline • target: 0.95 • range: 0-1

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

  • Develop autonomous multi-turn agents using the OpenAI Agents SDK

  • Implement native function calling for policy limit database queries

  • Apply automated fraud detection heuristics on medical invoice receipts

  • Output standardized payout authorization or human-adjuster escalation payloads

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic insurance claims data and medical bill receipt records.

How this agent runs

Evaluates OpenAI Agents SDK agent accuracy in triaging claims, identifying fraudulent billing indicators, and deciding settlement routing.

Challenge input

JSON object with claim_id, policy_num, claimed_amount_sgd, diagnosis_code, hospital_bill_url

OpenAI Agents SDK

Provides multi-agent handoffs and robust function calling capabilities

Evaluated output

JSON object with claim_status, approved_payout_sgd, fraud_risk_score, escalation_reason

Checks for
  • Ensures low-risk claim within policy limit is set to AUTO_APPROVED
  • Precision of fraud risk rating against synthetic evaluation baseline • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: InsurTriage-Bench
  • Fraud Score Accuracy target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Aim · Required
  • OpenAI · Optional
  • Factory AI · Optional

Evaluation contract

  • approval_logic_pass · Weight 1
  • fraud_score_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build an Automated Insurtech Claims Triaging System with OpenAI Agents SDK