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Challenge

M&A Financial Due Diligence Risk Analyst with CrewAI

Complex merchant payment acquisition deals collapse when operational risk and cyber liability exposure remain undetected during due diligence. You will build a multi-agent crew that analyzes financial disclosures and breach histories, generating quantitative risk assessment reports with verified evidence links within automated audit pipelines.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Orchestrate role-playing financial risk and cyber liability agents in CrewAI to audit deal disclosures for fintech acquisitions.

Delivery guide

How work is evaluated

Evaluation

Evaluates CrewAI team report output for identification of undisclosed liability risk items in deal disclosures.

Datasets

Challenge datasets

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

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

detects_breach_flag

Ensures breach liability is explicitly extracted in flagged_liabilities

Binary check

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

Dimension 2

risk_identification_recall

Recall metric of target liabilities identified • target: 0.95 • range: 0.85-1

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

  • Configure collaborative multi-agent tasks in CrewAI with custom tools

  • Assign distinct specialist roles (Financial Auditor, Cyber Risk Analyst, Legal Compliance Officer)

  • Process unstructured regulatory filings, loss run reports, and breach records

  • Generate structured merger and acquisition risk scores with actionable findings

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 20 simulated fintech acquisition disclosure packages containing embedded regulatory fines, breach logs, and financial disclosures.

Challenge setup

Evaluates CrewAI team report output for identification of undisclosed liability risk items in deal disclosures.

Challenge input

JSON containing array of deal disclosure document extracts

CrewAI

Framework for orchestrating role-playing multi-agent collaboration.

Evaluated output

JSON with overall_risk_score, flagged_liabilities, and approval_recommendation

Evaluation checks
  • Ensures breach liability is explicitly extracted in flagged_liabilities
  • Recall metric of target liabilities identified • target: 0.95 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: MA-DueDiligence-Bench
  • Risk Identification Recall target: 0.95
  • 1 public reference case
Configured runtime
  • 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.

View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional

Evaluation contract

  • detects_breach_flag · Weight 1
  • risk_identification_recall · Weight 1

Metrics

  • risk_identification_recall · Target 0.95

Benchmarks

  • MA-DueDiligence-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing array of deal disclosure document extracts

    Output: JSON with overall_risk_score, flagged_liabilities, and approval_recommendation

    Execution harness: Python

Evaluation tools

  • CrewAI · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about M&A Financial Due Diligence Risk Analyst with CrewAI