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Challenge

Build a COSMIC Anti-Money Laundering Sharing Agent with Google ADK

Financial crime teams struggle to cross-analyze collaborative intelligence without exposing raw PII. Build a Google ADK agent integrated with Helicone observability that parses anonymized COSMIC sharing logs to flag cross-bank structuring patterns, achieving a 95% detection accuracy on synthetic financial intelligence datasets.

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Challenge brief

What you are building

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

Develop an anti-money laundering analytics agent using Google ADK and Helicone to analyze collaborative banking signals without privacy leakage.

Delivery guide

How work is evaluated

Evaluation

Evaluates the agent's accuracy in identifying structured AML rings and compliance with audit logging.

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_validation

Ensures output conforms to the structured JSON risk schema.

Binary check

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

Dimension 2

detection_precision

Precision score of correctly identified AML network rings. • target: 0.95 • range: 0-1

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

  • Master privacy-preserving data extraction using Google ADK tools

  • Implement multi-bank transaction graph reconstruction for AML monitoring

  • Integrate Helicone observability for audit tracing and latency optimization

  • Build regulator-friendly explainable anomaly detection reports

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic multi-bank inter-institution financial log dataset mirroring COSMIC data structures.

Challenge setup

Evaluates the agent's accuracy in identifying structured AML rings and compliance with audit logging.

Challenge input

JSON formatted inter-bank transaction logs

Google ADK

Agent orchestration with native Gemini capabilities.

Helicone

Tracing prompt outputs and latency for regulatory compliance.

Evaluated output

JSON risk assessment report with flagged account IDs and confidence scores

Evaluation checks
  • Ensures output conforms to the structured JSON risk schema.
  • Precision score of correctly identified AML network rings. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: FinCrimeAgentBench
  • Detection Precision 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
  • Google ADK · Required
  • Google · Optional
Observation
  • Helicone · Optional

Evaluation contract

  • schema_validation · Weight 1
  • detection_precision · Weight 1

Metrics

  • detection_precision · Target 0.95

Benchmarks

  • FinCrimeAgentBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON formatted inter-bank transaction logs

    Output: JSON risk assessment report with flagged account IDs and confidence scores

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Framework
  • Helicone · Observability

Recipe freeze marker

No recipe freeze time is recorded.

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