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Challenge

Construct a Corporate Bank Onboarding AML Workflow with LangChain

Commercial banks struggle with prolonged onboarding timelines due to complex corporate structure verification and AML checks. Build a LangChain and LangGraph workflow that parses corporate ownership trees, executes sanctions screening, and generates MAS-compliant onboarding recommendations with zero missing mandatory audit steps.

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Challenge brief

What you are building

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

Develop a stateful LangGraph onboarding workflow using LangChain to automate corporate bank account AML verification and risk scoring.

Delivery guide

How work is evaluated

Evaluation

Evaluates completeness of AML audit checks and accuracy of ultimate beneficial owner identification.

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

audit_trail_complete

Ensures no required compliance step was bypassed in the state graph.

Binary check

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

Dimension 2

ubo_accuracy

Accuracy in resolving ultimate human beneficial owners. • target: 0.98 • 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 LangGraph state machine constructs for multi-step banking workflows

  • Implement custom LangChain tools for ultimate beneficial owner (UBO) resolution

  • Enforce compliance checks against regulatory sanctions datasets

  • Design zero-loss audit trails for corporate banking account approvals

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 40 corporate ownership trees with complex offshore holding structures.

Challenge setup

Evaluates completeness of AML audit checks and accuracy of ultimate beneficial owner identification.

Challenge input

JSON corporate ownership graph structure

LangChain

Popular framework featuring LangGraph for stateful multi-step enterprise chains.

Evaluated output

JSON audit decision with resolved UBO list and risk assessment

Evaluation checks
  • Ensures no required compliance step was bypassed in the state graph.
  • Accuracy in resolving ultimate human beneficial owners. • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: KYCOnboardingBench
  • Ubo Accuracy target: 0.98
  • 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
  • Langchain · Required
  • LangChain · Optional
  • AutoGluon · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • audit_trail_complete · Weight 1
  • ubo_accuracy · Weight 1

Metrics

  • ubo_accuracy · Target 0.98

Benchmarks

  • KYCOnboardingBench
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON corporate ownership graph structure

    Output: JSON audit decision with resolved UBO list and risk assessment

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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