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Build a Hedge Fund Compliance and Capital Allocation Pipeline with AutoGen

Design an automated regulatory reporting and risk management agent system for Singapore hedge fund managers. Using AutoGen multi-agent collaboration, separate agents will monitor MAS risk compliance, anchor decision logic, and draft investor risk disclaimers automatically.

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What you are building

Deploy an AutoGen multi-agent framework to audit hedge fund portfolio leverage and generate MAS regulatory compliance filings.

How work is evaluated

Evaluation

Evaluate multi-agent consensus on fund leverage compliance and MAS disclosure generation.

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

compliance_flag_test

Correctly flags leverage > 3.0x as non-compliant

Binary check

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

Dimension 2

report_completeness

Inclusion of all required MAS risk disclosure sections • target: 0.95 • range: 0-1

Binary check

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

Learning goals

  • Configure AutoGen multi-agent conversation topologies for compliance verification

  • Automate portfolio value-at-risk (VaR) calculations against MAS exposure limits

  • Build dynamic policy check functions for fund manager key-person risk tracking

  • Produce validated MAS SFA compliance reports with full audit traces

Resources

Dataset notes

Anonymized fund balance sheet records and portfolio asset allocation snapshots.

Challenge setup

Evaluate multi-agent consensus on fund leverage compliance and MAS disclosure generation.

Challenge input

JSON containing total_assets, gross_exposure, fund_name

AutoGen

Microsoft agent framework for multi-persona financial review.

Evaluated output

JSON containing leverage_ratio, mas_compliant (bool), remediation_required

Evaluation checks
  • Correctly flags leverage > 3.0x as non-compliant
  • Inclusion of all required MAS risk disclosure sections • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: FinTech_RegCompliance_Bench
  • Report Completeness target: 0.95
  • 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
  • AutoGen · Required
  • Aim · Optional
  • LatticeFlow AI · Optional

Evaluation contract

  • compliance_flag_test · Weight 1
  • report_completeness · Weight 1

Metrics

  • report_completeness · Target 0.95

Benchmarks

  • FinTech_RegCompliance_Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing total_assets, gross_exposure, fund_name

    Output: JSON containing leverage_ratio, mas_compliant (bool), remediation_required

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Hedge Fund Compliance and Capital Allocation Pipeline with AutoGen