AI normalization processes private company financials 86% faster than manual methods. Learn how illuminis Deal Intelligence cleans messy books for accurate M&A
Private companies, particularly those in the midmarket with 10 to 4,000 employees, frequently maintain financial records that present significant challenges for M&A evaluation. Unlike public companies subject to SEC reporting requirements and annual audits, private businesses often operate with compiled or reviewed financial statements, inconsistent chart of accounts structures, commingled personal and business expenses,and accounting policies chosen for tax minimization rather than GAAP accuracy. A 2025 survey of M&A advisors found that 78% of midmarket transactions require significant financial statement normalization before meaningful analysis can begin.
illuminis Deal Intelligence automates financial statement normalization for private company acquisition targets, processing messy books 86% faster than traditional manual methods while identifying normalization adjustments that human analysts frequently overlook. This acceleration is particularly valuable in competitive auction processes where evaluation speed directly impacts the ability to submit timely indications of interest.
The normalization challenges Deal Intelligence addresses span the full spectrum of private company financial irregularities:
Deal Intelligence processes financial data from the target company's accounting platform, whether QuickBooks, Xero, NetSuite, Sage, or others,and applies a systematic normalization framework. The platform maps the target's chart of accounts to a standardized structure, reclassifying accounts as needed to enable consistent analysis. It then scans every general ledger entry for normalization indicators including unusual amounts, irregular timing, related-party payees,and category anomalies.
The AI models have been trained on thousands of private company normalizations, enabling recognition of expense patterns that commonly indicate personal items, above-market related-party transactions,and one-time events that may not be explicitly labeled as such in the accounting records. Deal Intelligence generates a comprehensive normalization schedule with each adjustment categorized, explained,and quantified, ready for deal team review and discussion with the target company's management.
Every AI-generated normalization adjustment includes a confidence score and supporting evidence from the underlying data, enabling deal teams to efficiently review and validate the platform's findings. Adjustments with lower confidence scores are flagged for additional human review, while high-confidence adjustments can be incorporated into preliminary financial models immediately. This tiered review approach reduces total normalization time by 86% while maintaining the analytical rigor that deal teams and their clients require.
Normalized financial statements flow directly into Deal Intelligence's valuation and quality of earnings modules, ensuring consistency between the normalization assumptions and the financial analysis they support. For PE firms and M&A advisors, this integration eliminates the reconciliation errors that commonly occur when normalization and analysis are performed in separate workstreams or by different team members. The platform's end-to-end approach from raw financial data to normalized earnings to preliminary valuation provides deal teams with a defensible analytical chain that accelerates decision-making and strengthens negotiation positioning.