AI for Financial Due Diligence — Automate Quality of Earnings
Published February 2026
AI doesn't replace the analyst — it gives every analyst the throughput of a team. Screen more deals, catch more issues, close faster.
2–4 hrs
Initial analysis time
100%
Transaction coverage (not sampling)
$2,000
Starting price per project
Overview
AI-powered financial due diligence applies machine learning, natural language processing, and structured analysis to the Quality of Earnings process. Instead of analysts manually reviewing thousands of transactions, AI handles the data-intensive work while humans focus on judgment, context, and deal strategy.
What AI Automates in Due Diligence
Account mapping & classification
Automatically maps chart of accounts to standardized QoE categories — a task that typically takes days
EBITDA adjustment identification
Scans the GL for non-recurring items, personal expenses, and potential add-backs using pattern recognition
Anomaly detection
Identifies unusual transactions, round-dollar entries, duplicates, and period-end clustering across the entire ledger
Revenue quality scoring
Analyzes customer concentration, recurring vs non-recurring classification, and trend patterns
Working capital calculation
Builds multi-period NWC schedules with turnover ratios and peg calculations automatically
Proof of cash reconciliation
Matches GL activity to bank statement data to identify unrecorded transactions
AI-Assisted Due Diligence Workflow
Connect data sources
Upload financial statements, connect QuickBooks, or import trial balance data
AI processes & maps
Automatic account classification, period alignment, and data normalization
Review flagged items
AI surfaces potential adjustments, anomalies, and red flags for analyst review
Apply professional judgment
Accept, modify, or reject AI suggestions based on deal context and expertise
Generate deliverables
Produce lender-ready QoE reports, EBITDA bridges, and working capital schedules
GL-Level AI Analysis
The general ledger is where AI delivers the most dramatic improvement over manual processes:
Full coverage
Every transaction reviewed — not a sample. AI doesn't get tired or skip entries
Pattern detection
Identifies suspicious patterns across thousands of transactions that human reviewers would miss
Keyword intelligence
NLP-powered search for personal expenses, related-party transactions, and unusual descriptions
Cross-account analysis
Correlates activity across accounts to identify reclassification opportunities and intercompany flows
AI vs Traditional Due Diligence
| Dimension | Traditional CPA | AI-Assisted |
|---|---|---|
| Timeline | 4+ weeks | Days (initial analysis in hours) |
| Cost | $20K+ | Starting at $2,000 |
| Transaction coverage | Sample-based | 100% of GL |
| Consistency | Analyst-dependent | Standardized framework |
| Scalability | Linear (more deals = more staff) | Parallel processing |
| Professional judgment | Senior partner review | Human analyst + AI insights |
| Certifiable opinion | Yes (CPA attestation) | No (analysis assistance) |
When to Use AI Due Diligence
Deal screening
Quickly evaluate whether a target's financials warrant deeper investigation — before committing $30K+ to a CPA firm
Lower middle market
Deals under $10M where traditional QoE costs are disproportionate to deal size
Sell-side preparation
Sellers preparing for due diligence — identify and address issues before buyers find them
Time-critical deals
Competitive situations where speed is a strategic advantage
Portfolio monitoring
PE firms monitoring existing portfolio company performance
Complement to CPA
Use AI for initial analysis, then engage a CPA firm for formal attestation if needed
Honest Limitations
We believe in transparency about what AI can and cannot do:
No attestation
AI-assisted analysis is not a certified audit or attestation. Lenders requiring formal CPA opinions still need a firm
Judgment-dependent items
Items requiring business context (Is this expense truly non-recurring?) need human judgment
Data quality dependency
AI analysis is only as good as the input data — garbage in, garbage out applies
Complex structures
Multi-entity carve-outs, international transactions, and highly customized deals may need specialist expertise