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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.

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What you are building

Build a type-safe BNPL fraud scoring agent using Pydantic AI and monitor decision explainability with Fiddler AI observability.

How work is evaluated

Evaluation

Evaluates type enforcement and classification precision for buy-now-pay-later application reviews.

Datasets

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

How submissions are scored

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

schema_conformance

Ensures the response validates against BNPLRiskResult Pydantic class

Binary check

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

Dimension 2

precision

Precision of HIGH risk fraud identifications • 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

  • 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

Resources

Dataset notes

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

Evaluation checks
  • Ensures the response validates against BNPLRiskResult Pydantic class
  • Precision of HIGH risk fraud identifications • target: 0.97 • range: 0.92-1
Configured evaluation evidence
  • Benchmark: FraudEval-v2
  • Precision target: 0.97
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Fiddler AI · Optional
Orchestration
  • 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

  1. 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.

Frequently Asked Questions about BNPL Credit Fraud Anomaly Detection with Pydantic AI and Fiddler AI