Implement AI Fund Compliance and AML Entity Resolution for Asset Managers
Global asset management hubs demand frictionless onboarding of institutional funds with automated UBO discovery. Implement an OpenAI Agents SDK system that resolves ultimate beneficial owners across multi-jurisdictional corporate structures.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an entity resolution agent with OpenAI Agents SDK to parse multi-jurisdictional corporate ownership graphs and reveal ultimate beneficial owners.
How work is evaluated
Evaluates UBO resolution accuracy through multi-tiered corporate structure graphs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
25 Percent Threshold Flag Test
Individual holding >25% effective ownership must be designated as primary UBO.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
UBO Identification Accuracy
Accuracy of resolved ultimate beneficial ownership chains. • target: 1 • range: 0.95-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement multi-jurisdictional entity resolution using OpenAI Agents SDK.
Parse multi-layer shell company ownership trees to calculate Ultimate Beneficial Ownership (UBO) percentages.
Cross-reference offshore ownership entities against FATF high-risk jurisdiction matrices.
Achieve 100% detection of hidden control stakeholders holding >25% equity.
Reference links and supporting material
Synthetic multi-jurisdictional corporate hierarchy graph dataset containing offshore holding links.
Challenge setup
Evaluates UBO resolution accuracy through multi-tiered corporate structure graphs.
Challenge input
JSON containing target_entity, holding_companies_list, individual_shareholders
OpenAI Agents SDK
Provides function calling capabilities for dynamic corporate graph traversal.
Evaluated output
JSON with ubo_identified (boolean), primary_ubo_name, effective_ownership_pct
- Individual holding >25% effective ownership must be designated as primary UBO.
- Accuracy of resolved ultimate beneficial ownership chains. • target: 1 • range: 0.95-1
- Benchmark: UBODiscoveryBench
- UBO Identification Accuracy target: 1
- 1 public reference case
- 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.
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Configured tools
- OpenAI · Required
- Azure OpenAI Service · Optional
- OpenAI o3 · Optional
Evaluation contract
- 25 Percent Threshold Flag Test · Weight 1
- UBO Identification Accuracy · Weight 1
Metrics
- UBO Identification Accuracy · Target 1
Benchmarks
- UBODiscoveryBench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing target_entity, holding_companies_list, individual_shareholders
Output: JSON with ubo_identified (boolean), primary_ubo_name, effective_ownership_pct
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.