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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates false negative rate on international PEP and sanctions list screening.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

Zero False Pass Guarantee

Sanctioned/PEP individuals must never yield pass_flag == True.

Binary check

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

Dimension 2

Sanctions Match Accuracy

Percentage of accurate sanctions identifications. • target: 1 • range: 0.99-1

Binary check

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

Learning goals

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.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic cross-border payment stream between US and LatAm banking counterparties.

Challenge setup

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

Evaluation checks
  • Sanctioned/PEP individuals must never yield pass_flag == True.
  • Percentage of accurate sanctions identifications. • target: 1 • range: 0.99-1
Configured evaluation evidence
  • Benchmark: CrossBorderAMLBench
  • Sanctions Match Accuracy target: 1
  • 1 public reference case
Configured runtime
  • 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

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

Evaluation contract

  • Zero False Pass Guarantee · Weight 1
  • Sanctions Match Accuracy · Weight 1

Metrics

  • Sanctions Match Accuracy · Target 1

Benchmarks

  • CrossBorderAMLBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with sender_details, beneficiary_details, origin_country, destination_country

    Output: JSON validated by Pydantic model with pass_flag (boolean), match_details

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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