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Crypto Banking Off-Ramp AML Verification with Claude Agents SDK

Crypto-to-fiat banking off-ramps require rigorous multi-jurisdictional AML source-of-funds verification to satisfy banking partner compliance mandates. Build an off-ramp AML verification pipeline using Claude Agents SDK to trace wallet lineage, analyze fiat destination account legitimacy, and flag suspicious transactions with 96% accuracy.

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What you are building

Build an intelligent crypto off-ramp verification pipeline utilizing Anthropic Claude Agents SDK and tool use features.

How work is evaluated

Evaluation

Evaluates accuracy of wallet risk scoring and validity of generated SAR evidence packets.

Datasets

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

How submissions are scored

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

evidence_presence_check

Ensures frozen transactions contain non-empty evidence arrays.

Binary check

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

Dimension 2

aml_detection_accuracy

Accuracy in correctly flagging suspicious vs legitimate off-ramps. • target: 0.96 • 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

  • Construct autonomous verification workflows using Anthropic Claude Agents SDK

  • Implement multi-hop blockchain wallet taint analysis tools for off-ramp requests

  • Validate fiat destination bank details against global IBAN and SWIFT registers

  • Generate human-readable investigator audit trails detailing risk scoring rationale

Resources

Dataset notes

Synthetic dataset of 400 crypto-to-fiat transfer requests with simulated wallet transaction graphs and bank account verifications.

Challenge setup

Evaluates accuracy of wallet risk scoring and validity of generated SAR evidence packets.

Challenge input

JSON transaction request with origin wallet address, asset, fiat payout bank, and amount

Claude Agents SDK

Anthropic's SDK supporting tool use and deep reasoning capability.

Evaluated output

JSON verification object with risk_tier, decision, and detailed_evidence array

Evaluation checks
  • Ensures frozen transactions contain non-empty evidence arrays.
  • Accuracy in correctly flagging suspicious vs legitimate off-ramps. • target: 0.96 • range: 0-1
Configured evaluation evidence
  • Benchmark: CryptoAML-Bench-v1
  • Aml Detection Accuracy target: 0.96
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

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

Evaluation contract

  • evidence_presence_check · Weight 1
  • aml_detection_accuracy · Weight 1

Metrics

  • aml_detection_accuracy · Target 0.96

Benchmarks

  • CryptoAML-Bench-v1
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON transaction request with origin wallet address, asset, fiat payout bank, and amount

    Output: JSON verification object with risk_tier, decision, and detailed_evidence array

    Execution harness: Python

Evaluation tools

  • Claude Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Crypto Banking Off-Ramp AML Verification with Claude Agents SDK