BNPL Credit Fraud Anomaly Detection with Pydantic AI and Fiddler AI
Rapid expansion in buy-now-pay-later services exposes financial networks to sophisticated identity spoofing and synthetic fraud tactics. You will construct a type-safe fraud risk scoring workflow that validates applicant telemetry and flags high-risk transactions with verifiable confidence metrics and zero schema validation errors.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a type-safe BNPL fraud scoring agent using Pydantic AI and monitor decision explainability with Fiddler AI observability.
How work is evaluated
Evaluates type enforcement and classification precision for buy-now-pay-later application reviews.
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.
schema_conformance
Ensures the response validates against BNPLRiskResult Pydantic class
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
precision
Precision of HIGH risk fraud identifications • target: 0.97 • range: 0.92-1
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 structured Pydantic AI agents for type-safe decision pipelines
Integrate Fiddler AI model monitoring to trace feature drift and explainability metrics
Analyze BNPL checkout telemetry for identity theft and loan stacking signals
Achieve zero validation runtime exceptions across dynamic application payloads
Reference links and supporting material
2,000 synthetic buy-now-pay-later checkout sessions featuring device intelligence, credit soft-pull data, and fraud labels.
Challenge setup
Evaluates type enforcement and classification precision for buy-now-pay-later application reviews.
Challenge input
JSON payload containing checkout session telemetry
Pydantic AI
Type-safe agent definition and schema validation framework.
Fiddler AI
Provides operational observability and model explainability tracking.
Evaluated output
Validated Pydantic model output containing risk_tier, risk_score, and key_factors
- Ensures the response validates against BNPLRiskResult Pydantic class
- Precision of HIGH risk fraud identifications • target: 0.97 • range: 0.92-1
- Benchmark: FraudEval-v2
- Precision target: 0.97
- 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.
View technical recipe
Configured tools
- Pydantic AI · Required
- Fiddler AI · Optional
- Pydantic AI · Required
Evaluation contract
- schema_conformance · Weight 1
- precision · Weight 1
Metrics
- precision · Target 0.97
Benchmarks
- FraudEval-v2
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON payload containing checkout session telemetry
Output: Validated Pydantic model output containing risk_tier, risk_score, and key_factors
Execution harness: Python
Evaluation tools
- Pydantic AI · agent_framework
- Fiddler AI · ai_workflow_automation
Recipe freeze marker
No recipe freeze time is recorded.