Credit Card Default Risk & Intervention Agent with OpenAI Agents SDK
UK credit card defaults have surged to a 17-year high, requiring financial institutions to proactively identify distressed cardholders before charge-off occurs. Build an early intervention risk management agent using OpenAI Agents SDK to analyze consumer debt accumulation signals and generate customized repayment assistance plans, reducing default rates by 18%.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement an autonomous credit monitoring agent using OpenAI Agents SDK with function calling to assess consumer balance trends and trigger timely debt restructuring plans.
How work is evaluated
Evaluates accuracy of default prediction risk tiering and compliance of generated intervention plans.
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.
FCA Compliance Check
Ensure generated plans do not propose punitive interest rates above legal limits.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
default_prediction_accuracy
Accuracy in categorizing high-risk default borrowers • target: 0.92 • range: 0.85-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement OpenAI Agents SDK tools for real-time credit utilization analysis
Design personalized debt forbearance strategy recommendations
Establish automated customer outreach triggers based on credit score trend vectors
Reference links and supporting material
Synthetic panel dataset of 10,000 UK consumer credit card account histories tracking 24 months of payment behavior.
Challenge setup
Evaluates accuracy of default prediction risk tiering and compliance of generated intervention plans.
Challenge input
JSON object with monthly balances, payment history, and credit utilization
OpenAI Agents SDK
Mandated agentic framework for OpenAI models.
Evaluated output
JSON output with assigned risk tier (LOW/MEDIUM/HIGH/CRITICAL) and proposed plan
- Ensure generated plans do not propose punitive interest rates above legal limits.
- Accuracy in categorizing high-risk default borrowers • target: 0.92 • range: 0.85-1
- Benchmark: CreditRisk-Bench
- Default Prediction Accuracy target: 0.92
- 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
- OpenAI · Required
- Azure OpenAI Service · Optional
- OpenAI o3 · Optional
Evaluation contract
- FCA Compliance Check · Weight 1
- default_prediction_accuracy · Weight 1
Metrics
- default_prediction_accuracy · Target 0.92
Benchmarks
- CreditRisk-Bench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with monthly balances, payment history, and credit utilization
Output: JSON output with assigned risk tier (LOW/MEDIUM/HIGH/CRITICAL) and proposed plan
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
Recipe freeze marker
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