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.
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.
How work is evaluated
Evaluates CrewAI team report output for identification of undisclosed liability risk items in deal disclosures.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
detects_breach_flag
Ensures breach liability is explicitly extracted in flagged_liabilities
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
risk_identification_recall
Recall metric of target liabilities identified • target: 0.95 • range: 0.85-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
Dataset of 20 simulated fintech acquisition disclosure packages containing embedded regulatory fines, breach logs, and financial disclosures.
How this agent runs
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
- Ensures breach liability is explicitly extracted in flagged_liabilities
- Recall metric of target liabilities identified • target: 0.95 • range: 0.85-1
- Benchmark: MA-DueDiligence-Bench
- Risk Identification Recall target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
Evaluation contract
- detects_breach_flag · Weight 1
- risk_identification_recall · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.