Challenge

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.

CybersecurityHosted by Vera
Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates UBO resolution accuracy through multi-tiered corporate structure graphs.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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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

25 Percent Threshold Flag Test

Individual holding >25% effective ownership must be designated as primary UBO.

Binary check

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

Dimension 2

UBO Identification Accuracy

Accuracy of resolved ultimate beneficial ownership chains. • target: 1 • range: 0.95-1

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

  • 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.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic multi-jurisdictional corporate hierarchy graph dataset containing offshore holding links.

How this agent runs

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

Checks for
  • Individual holding >25% effective ownership must be designated as primary UBO.
  • Accuracy of resolved ultimate beneficial ownership chains. • target: 1 • range: 0.95-1
Proof of success
  • Benchmark: UBODiscoveryBench
  • UBO Identification Accuracy target: 1
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Azure OpenAI Service · Optional
  • OpenAI o3 · Optional

Evaluation contract

  • 25 Percent Threshold Flag Test · Weight 1
  • UBO Identification Accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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