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
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.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
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.
Learning goals
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
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
- 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)
Shows the first task, up to three tools, and up to three evidence items.
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
Metrics
- ubo_accuracy · Target 0.98
Benchmarks
- KYCOnboardingBench
- security
Reference cases
- 1 public reference case
Workflow tasks
- 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.