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Challenge

Build a COSMIC Cross-Bank Anti-Money Laundering Pipeline with LangChain

Financial institutions require automated threat sharing without compromising customer privacy regulations under Singapore's COSMIC framework. Build a stateful LangGraph workflow using Vercel AI Gateway to aggregate cross-bank suspicious activity reports into standardized, audit-ready COSMIC intelligence assessments.

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Challenge brief

What you are building

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

Construct a stateful LangGraph workflow routed through Vercel AI Gateway to aggregate cross-bank suspicious activity reports into standardized COSMIC risk evaluations.

Delivery guide

How work is evaluated

Evaluation

Evaluates the LangGraph pipeline's ability to ingest transaction logs, redact PII, and generate structured COSMIC risk reports matching target ground truth.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

pii_redaction_check

Verifies no raw customer personal data is leaked in the output payload

Binary check

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

Dimension 2

accuracy

F1 score of identified suspicious accounts against ground truth • target: 0.85 • 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

  • Design a stateful multi-step graph workflow with LangChain and LangGraph

  • Route multi-model LLM calls through Vercel AI Gateway with fallback policies

  • Implement data sanitization and privacy-preserving entity resolution for banking records

  • Generate structured COSMIC-compliant AML threat indicators with high precision

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic multi-bank ledger datasets containing anomalous payment flows and obfuscated identity markers.

How this agent runs

Evaluates the LangGraph pipeline's ability to ingest transaction logs, redact PII, and generate structured COSMIC risk reports matching target ground truth.

Challenge input

JSON array of raw multi-bank transaction objects with suspect account IDs

LangChain

Provides stateful node execution via LangGraph

Vercel AI Gateway

Offers unified model routing and caching

Evaluated output

JSON object containing risk_score, flagged_entities, privacy_status, and cosmic_report_payload

Checks for
  • Verifies no raw customer personal data is leaked in the output payload
  • F1 score of identified suspicious accounts against ground truth • target: 0.85 • range: 0-1
Proof of success
  • Benchmark: FinRisk-Bench-2026
  • Accuracy target: 0.85
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • LangChain · Required
  • Langchain · Optional
Policy Serving
  • Vercel AI Gateway · Optional
Orchestration
  • LangChain · Required
  • Langchain · Optional
  • Vercel AI Gateway · Optional

Evaluation contract

  • pii_redaction_check · Weight 1
  • accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build a COSMIC Cross-Bank Anti-Money Laundering Pipeline with LangChain