AI-Powered Quality of Earnings & Financial Due Diligence
An AI-powered Quality of Earnings platform combining financial anomaly detection, adjustment intelligence, document intelligence and evidence-driven workflows for financial due diligence teams analyzing middle-market M&A transactions.
| Date | Account | Amount | Type |
|---|---|---|---|
| 10/12/23 | Consulting Fees | $145,000 | Debit |
| 10/18/23 | Management Bonus | $220,000 | Debit |
| 11/04/23 | Legal & Settlement | $350,000 | Debit |
| 11/15/23 | Warehouse CapEx | $420,000 | Debit |
Detecting anomalies…
Mapping accounts…
Linking supporting documents…
Generating adjustment ideas…
Anomaly Detection Rules
Automated financial analysis across monthly and transaction-level data
Connected QoE Pillars
Financial Analysis → Adjustment Intelligence → Evidence & Questions
Multi-Year Financial Data
GL, Trial Balance, payroll and supporting transaction documents
Offline LLM Processing
Sensitive financial information processed inside the customer’s controlled environment
What is AI-powered Quality of Earnings automation?
AI-powered Quality of Earnings automation uses artificial intelligence, financial anomaly detection, document intelligence and analyst review workflows to identify potential QoE adjustments, connect findings to supporting evidence and streamline Adjusted EBITDA analysis during financial due diligence.
A Smarter Way to Approach Quality of Earnings
Manual analysis of large financial datasets, supporting documents and potential QoE adjustments.
AI-assisted anomaly detection, adjustment classification, document intelligence and evidence-driven review.
A structured workflow connecting financial data, findings, evidence and validated QoE adjustments.
The Challenge
Financial due diligence teams typically spend significant manual effort reviewing large volumes of financial data and supporting documents. Important adjustments can be missed due to the time-consuming and fragmented nature of the process.
Traditional QoE Analysis vs. Cognic AI-Powered Workflow
| Due Diligence Stage | Traditional Manual Workflow | Cognic AI-Powered QoE Workflow |
|---|---|---|
| General Ledger Analysis | Manual filtering and review of large GL datasets | Automated financial anomaly detection across monthly and transaction-level data |
| Adjustment Clustering | Analysts manually identify and group potential adjustments | Findings classified into adjustment clusters using account, transaction, vendor, memo, timing and financial behavior |
| Evidence Linking | Supporting documents manually located and reviewed | Document intelligence connects supporting financial documents with proposed adjustments |
| Reviewer Validation | Findings maintained across spreadsheets and working papers | Analyst review with confidence scoring and validation |
| Adjusted EBITDA Bridge | Manually assembled from findings | Generated from validated QoE adjustments |
| Data Security | Depends on deployment and data-processing architecture | Offline LLM and controlled-environment deployment for sensitive financial data |
The Cognic Quality of Earnings Engine
Three connected pillars that turn financial data into defensible QoE adjustments.
Analyze financial data and identify unusual patterns across monthly and transaction-level data.
Classify findings into adjustment clusters based on:
Connect proposed adjustments with:
Validate findings with confidence scoring
Generate from validated adjustments
Export working papers and reports
Inside the Cognic QoE Platform
A modular architecture designed for secure, scalable financial due diligence.
From Financial Data to Defensible QoE Adjustment
Every adjustment in the platform maintains a traceable chain from financial data through findings, evidence and final QoE adjustment.
Financial
Data
→
Pillar 1
Finding
→
Pillar 2
Cluster
→
Adjustment
Type
→
Supporting
Evidence
→
Final QoE
Adjustment
AI / Automation Layer
Core AI capabilities that power the QoE platform.
Analyst Workbench
A purpose-built review interface for financial due diligence.
Built for Sensitive Financial Data
The platform uses an offline / air-gapped architecture for sensitive financial data processing.
Technology Stack
Core frameworks & integrations powering the engine.
What Changed
Key business and workflow transformations.
Built for Financial Due Diligence Teams
Target Audience / Who This Is For
Faster analysis during M&A transactions
Standardize review across engagements
Repeatable AI-assisted diligence workflow