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.
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.
How work is evaluated
Evaluates OpenAI Agents SDK agent accuracy in triaging claims, identifying fraudulent billing indicators, and deciding settlement routing.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
approval_logic_pass
Ensures low-risk claim within policy limit is set to AUTO_APPROVED
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
fraud_score_accuracy
Precision of fraud risk rating against synthetic evaluation baseline • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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
Reference links and supporting material
Synthetic insurance claims data and medical bill receipt records.
Challenge setup
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
- 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
- Benchmark: InsurTriage-Bench
- Fraud Score Accuracy target: 0.95
- 1 public reference case
- 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.
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Configured tools
- Aim · Required
- OpenAI · Optional
- Factory AI · Optional
Evaluation contract
- approval_logic_pass · Weight 1
- fraud_score_accuracy · Weight 1
Metrics
- fraud_score_accuracy · Target 0.95
Benchmarks
- InsurTriage-Bench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with claim_id, policy_num, claimed_amount_sgd, diagnosis_code, hospital_bill_url
Output: JSON object with claim_status, approved_payout_sgd, fraud_risk_score, escalation_reason
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Autonomous Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.