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.
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.
How work is evaluated
Evaluates completeness of AML audit checks and accuracy of ultimate beneficial owner identification.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
audit_trail_complete
Ensures no required compliance step was bypassed in the state graph.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ubo_accuracy
Accuracy in resolving ultimate human beneficial owners. • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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
Reference links and supporting material
Synthetic dataset of 40 corporate ownership trees with complex offshore holding structures.
How this agent runs
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
- Ensures no required compliance step was bypassed in the state graph.
- Accuracy in resolving ultimate human beneficial owners. • target: 0.98 • range: 0-1
- Benchmark: KYCOnboardingBench
- Ubo Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Langchain · Required
- LangChain · Optional
- AutoGluon · Optional
- Langchain · Required
- LangChain · Optional
Evaluation contract
- audit_trail_complete · Weight 1
- ubo_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.