Quality of Earnings Anomaly Finder
Studies financial statements, general ledger extracts, and revenue recognition policies to spot anomalies in reported earnings. Surfaces unsubstantiated adjustments, unusual revenue patterns, and working capital irregularities that may indicate earnings quality risks.
How It Works
Applies forensic accounting algorithms to multi-year financial data, cross-referencing general ledger entries against revenue recognition policies and industry benchmarks. The anomaly detection engine uses statistical outlier review and pattern recognition to spot suspicious adjustments, timing manipulations, and classification irregularities that warrant deeper investigation.
MPPT-CoT Execution Framework
Intake & Specification Lock
Secure data ingestion with schema checks and specification confirmation.
Evidence Kernel Retrieval
Cryptographic checks and provenance anchoring of all source data.
Multi-Branch Scenario Review
Parallel scenario forking across base, adverse, and adversarial conditions.
Evidence-Locked Deliverable
Board-ready output with complete audit trails and ownership mapping.
Key Performance Indicators
Source Documentation
Deliverable Outputs
Execute Quality of Earnings Anomaly Finder
Provide the required inputs below to initiate the MPPT-CoT review pipeline. Your data will be processed by our AI-powered review engine, producing genuinely tailored, evidence-locked deliverables specific to your submission.
Audited financial statements including income statement, balance sheet, and cash flow for 3+ years.
Detailed general ledger extracts with transaction-level data for the review period.
Relevant policies, procedures, or governance frameworks.
Industry benchmark data from recognized sources (Bain, McKinsey, PitchBook, Cambridge Associates, etc.).
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