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Regulatory Compliance Audit Agent for Synthetic Financial Assets

As decentralized protocols launch tokenized real-world assets (RWAs) and synthetic derivatives, financial regulators demand transparent risk disclosure, collateralization ratio auditing, and accredited investor verification before allowing public trading. In this challenge, you will construct an autonomous regulatory compliance auditing agent using the OpenAI Agents SDK. The agent will inspect synthetic asset smart contract parameters, verify backing collateral ratios, and evaluate compliance against regulatory leverage limits.

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What you are building

Build an OpenAI Agents SDK compliance auditor to verify tokenized real-world asset collateralization and leverage limits.

How work is evaluated

Evaluation

Evaluates collateral ratio checking accuracy and compliance pass/fail classifications.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

undercollateralized_halt_test

Ensures issuance is halted when ratio < 1.25

Binary check

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

Dimension 2

audit_classification_accuracy

Accuracy in identifying compliant vs non-compliant asset state • target: 100 • range: 0-100

Binary check

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

Learning goals

  • Initialize OpenAI Agents SDK agents with custom Web3 collateral query tools

  • Calculate real-time over-collateralization ratios for synthetic token assets

  • Enforce maximum leverage limits (e.g. 125% min collateralization) required by policy

  • Generate regulatory compliance sign-off certificates with zero-knowledge verification hashes

Resources

Dataset notes

Synthetic tokenized real-world asset smart contract telemetry and balance sheet records.

Challenge setup

Evaluates collateral ratio checking accuracy and compliance pass/fail classifications.

Challenge input

JSON containing asset_name, synthetic_value_usd, and collateral_value_usd

OpenAI Agents SDK

Official framework for multi-turn autonomous compliance agents.

Evaluated output

JSON containing collateral_ratio, compliance_pass, and action_taken

Evaluation checks
  • Ensures issuance is halted when ratio < 1.25
  • Accuracy in identifying compliant vs non-compliant asset state • target: 100 • range: 0-100
Configured evaluation evidence
  • Benchmark: FinDeFi-Compliance-Bench
  • Audit Classification Accuracy target: 100
  • 1 public reference case
Configured runtime
  • 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

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

Evaluation contract

  • undercollateralized_halt_test · Weight 1
  • audit_classification_accuracy · Weight 1

Metrics

  • audit_classification_accuracy · Target 100

Benchmarks

  • FinDeFi-Compliance-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing asset_name, synthetic_value_usd, and collateral_value_usd

    Output: JSON containing collateral_ratio, compliance_pass, and action_taken

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Regulatory Compliance Audit Agent for Synthetic Financial Assets