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Challenge

Build Real-Time Multi-Bank AML Information Sharing Agent with OpenAI Agents SDK

In response to initiatives like Singapore's COSMIC platform for collaborative information sharing to combat money laundering and terrorism financing (ML/TF), financial institutions require automated agentic triage systems. These systems must resolve complex multi-bank entity networks, identify suspicious shell company chains, and synthesize evidence for regulatory disclosures without compromising cross-border data privacy protocols.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an agentic anti-money laundering triage engine using OpenAI Agents SDK and Claude 4.1 Opus to evaluate cross-bank transaction topologies and flags potential money laundering networks.

Delivery guide

How work is evaluated

Evaluation

Evaluates the agent's capability to detect multi-bank layering schemes, extract entity relationships, and compute risk scores with strict schema accuracy.

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

schema_conformance

Output adheres exactly to expected JSON structure.

Binary check

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

Dimension 2

ring_detection_f1

F1 score for correctly identifying laundering rings. • target: 0.95 • range: 0.8-1

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

  • Implement multi-bank transaction topology analysis using OpenAI Agents SDK tool execution

  • Perform cross-entity disambiguation and graph traversal to uncover synthetic identity rings

  • Structure automated SAR (Suspicious Activity Report) narratives following FATF guidelines

  • Integrate Claude 4.1 Opus via fallback tool definitions for complex legal reasoning

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic multi-bank financial telemetry containing 10,000 accounts and 50,000 transactions with embedded circular pass-through and rapid movement anomalies.

Challenge setup

Evaluates the agent's capability to detect multi-bank layering schemes, extract entity relationships, and compute risk scores with strict schema accuracy.

Challenge input

JSON containing network graph nodes (accounts/entities) and edges (transactions).

OpenAI Agents SDK

Primary framework for tool invocation and state handling.

Claude 4.1 Opus

High-precision legal reasoning for automated SAR draft synthesis.

Evaluated output

JSON with detected_ring_id, risk_score, matched_patterns, and generated_sar_summary.

Evaluation checks
  • Output adheres exactly to expected JSON structure.
  • F1 score for correctly identifying laundering rings. • target: 0.95 • range: 0.8-1
Configured evaluation evidence
  • Benchmark: Financial CyberCrime Bench
  • Ring Detection F1 target: 0.95
  • 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.

View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Claude Code · Optional
Observation
  • Claude Code · Optional
Policy Serving
  • Claude 4.1 Opus · Optional

Evaluation contract

  • schema_conformance · Weight 1
  • ring_detection_f1 · Weight 1

Metrics

  • ring_detection_f1 · Target 0.95

Benchmarks

  • Financial CyberCrime Bench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing network graph nodes (accounts/entities) and edges (transactions).

    Output: JSON with detected_ring_id, risk_score, matched_patterns, and generated_sar_summary.

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Orchestration
  • Claude 4.1 Opus · Legal & Compliance LLM

Recipe freeze marker

No recipe freeze time is recorded.

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