Implement Cross-Border Cross-Currency AML Risk Screening using Pydantic AI
International bank expansions require strict cross-jurisdictional compliance screening across BSA and LatAm regulatory frameworks. Develop a type-safe Pydantic AI agent that screens transaction flows against global PEP and sanction lists with 0% false pass rates.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe Python compliance agent with Pydantic AI to enforce multi-jurisdictional AML validation rules.
How work is evaluated
Evaluates false negative rate on international PEP and sanctions list screening.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Zero False Pass Guarantee
Sanctioned/PEP individuals must never yield pass_flag == True.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Sanctions Match Accuracy
Percentage of accurate sanctions identifications. • target: 1 • range: 0.99-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a Pydantic AI agent leveraging strong Pydantic runtime model validation.
Integrate entity resolution tools for cross-referencing US and Brazilian PEP databases.
Enforce strict schema validation on output AML risk classifications.
Ensure zero false negative passes for confirmed sanctions list entities.
Reference links and supporting material
Synthetic cross-border payment stream between US and LatAm banking counterparties.
How this agent runs
Evaluates false negative rate on international PEP and sanctions list screening.
Challenge input
JSON with sender_details, beneficiary_details, origin_country, destination_country
Pydantic AI
Provides type-safe agent execution with Pydantic model validation.
Evaluated output
JSON validated by Pydantic model with pass_flag (boolean), match_details
- Sanctioned/PEP individuals must never yield pass_flag == True.
- Percentage of accurate sanctions identifications. • target: 1 • range: 0.99-1
- Benchmark: CrossBorderAMLBench
- Sanctions Match Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Pydantic AI · Required
- Pydantic AI · Required
Evaluation contract
- Zero False Pass Guarantee · Weight 1
- Sanctions Match Accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.