AI-Powered Quality of Earnings Automation for Financial Due Diligence

CUSTOMER

A financial advisory and due diligence team performing Quality of Earnings analysis for middle-market M&A transactions.

The team needed to analyze large volumes of Trial Balance, General Ledger, payroll, invoice, bill, and supporting financial data to identify unusual transactions and potential EBITDA adjustments.

CHALLENGE

Traditional Quality of Earnings analysis requires analysts to manually review financial data, identify unusual transactions, investigate potential adjustments, and connect each adjustment to supporting evidence.

The key challenges included:

• Large General Ledger datasets with thousands of transactions
• Multiple years of monthly financial data
• Difficulty identifying unusual expenses and revenue movements
• Manual identification of one-time and non-recurring costs
• Difficulty separating transaction costs, payroll adjustments, professional fees, and owner-related expenses
• Limited visibility when only Trial Balance data was available
• Need to connect financial anomalies to individual GL transactions
• Need to support each proposed adjustment with evidence
• Need for consistent confidence scoring across adjustments
• Manual preparation of the Adjusted EBITDA bridge
• Strict data security requirements for sensitive financial and transaction information

COGNIC’S SOLUTION

Cognic Systems developed an AI-powered Quality of Earnings analysis platform that combines financial anomaly detection, transaction classification, adjustment mapping, document intelligence, and evidence-based confidence scoring.

The solution uses a multi-pillar approach.

Pillar 1 analyzes financial data and detects unusual patterns across monthly and transaction-level data using 16 financial anomaly detection rules.

Pillar 2 classifies identified findings into adjustment clusters based on account, transaction, vendor, memo, timing, and financial behavior.

Pillar 3 connects proposed adjustments with supporting evidence such as invoices, bills, contracts, payroll records, settlement documents, and other financial documents.

The platform also uses an offline Large Language Model for sensitive financial data processing.

Instead of sending confidential GL, payroll, vendor, transaction, and due diligence information to a public AI service, the LLM runs within the customer’s controlled environment.

This architecture provides an additional layer of data protection for sensitive financial information while still supporting AI-based analysis, classification, document understanding, and financial reasoning.

The platform also maintains a traceable relationship between:

Financial Data → Pillar 1 Finding → Pillar 2 Cluster → Adjustment Type → Supporting Evidence → Final QoE Adjustment

Cognic implemented a confidence scoring framework using signals such as GL account mapping, vendor matching, memo keywords, anomaly magnitude, variance type, timing alignment, supporting documents, payroll records, and related-party indicators.

BUSINESS BENEFITS

• Faster identification of potential Quality of Earnings adjustments
• Automated analysis of large GL and Trial Balance datasets
• Consistent financial anomaly detection across transactions
• Automated classification of potential EBITDA adjustments
• Clear connection between Pillar 1 findings and Pillar 2 adjustments
• Evidence-based confidence scoring
• Traceability from adjustment to source transaction and supporting document
• Reduced manual financial data review
• Improved consistency across QoE engagements
• Faster preparation of the Adjusted EBITDA bridge
• Better visibility into one-time, non-operating, and owner-related expenses
• Stronger audit trail for analyst and reviewer validation
• Improved control over sensitive financial information
• Offline AI processing for environments with strict data security requirements

TECHNOLOGY USED

• Artificial Intelligence
• Generative AI
• Offline Large Language Models
• AI Agents
• Financial Data Analytics
• General Ledger Analysis
• Trial Balance Analysis
• Anomaly Detection
• Rule-Based Financial Analysis
• Quality of Earnings Automation
• EBITDA Adjustment Engine
• Document Intelligence
• OCR
• Natural Language Processing
• Retrieval-Augmented Generation
• Vector Search
• PostgreSQL
• MongoDB
• REST APIs
• React
• .NET
• Power BI
• Secure Cloud and On-Premise Deployment