Harnessing AI for Smarter Forensic Audits
Author: CA . MADHURI MURALIDHAR
Introduction
Forensic audits are crucial for uncovering financial manipulation, but traditional methods often struggle under the weight of massive data volumes.
Preliminary analysis of financial statements forms the foundation for the future course of any forensic audit. Manual reviews are slow, prone to human error, and can easily overlook subtle warning signs hidden across years of records.
Problem Statement
Traditional forensic audits face three key challenges:
- Voluminous Data: Analyzing years of financial data manually is inefficient and error-prone.
- Time Constraints: Manual reviews cause delays in audit planning and strategic decision-making — and time is a critical factor in forensic audits.
- Complex Risk Detection: Spotting subtle anomalies quickly demands advanced analytical capabilities that traditional methods often lack.
For instance, calculating the Beneish M-Score—a model that detects earnings manipulation using eight financial ratios —involves intricate formulas and cross-year comparisons. Manual computation risks miscalculations and oversight, especially when scaling across decades of data.
Why the Beneish M-Score Matters in Forensic Audits
Developed by Professor Messod D. Beneish, the Beneish M-Score is a powerful tool for identifying earnings manipulation.
It distills eight financial ratios into a single score: the higher the score, the greater the likelihood of manipulation.
Components of the Beneish M-Score
| Component | What It Measures | Relevance |
| Component | What It Measures | Relevance |
| DSRI (Days Sales in Receivables Index) | Compares the current year's days sales outstanding to the previous year's. | A significant increase suggests accelerated revenue recognition (e.g., shipping products before payment) to inflate profits. |
| GMI (Gross Margin Index) | Compares the current year's gross margin to the previous year's. | Declining margins may incentivize profit inflation through financial statement manipulation. |
| AQI (Asset Quality Index) | Assesses the proportion of total assets that may lack future benefits. | An increase suggests deferred expenses (e.g., capitalizing costs instead of expensing) to inflate earnings. |
| SGI (Sales Growth Index) | Compares the current year's sales to the previous year's. | Rapid growth increases pressure to meet earnings targets, raising the likelihood of manipulation. |
| DEPI (Depreciation Index) | Compares the current year's depreciation expense to the previous year's. | A decrease may indicate revised asset useful life or methods to overstate profits. |
| SGAI (SG&A Expenses Index) | Compares the current year's SG&A expenses to the previous year's. | A significant increase may signal inflated expenses to artificially lower profits. |
| LVGI (Leverage Index) | Assesses the proportion of debt to total assets. | Changes in leverage can mask liquidity issues or enable earnings manipulation via debt strategies. |
| TATA (Total Accruals to Total Assets) | Measures the difference between reported income and cash flow from operations. | High accruals suggest earnings are not cash-backed, indicating potential manipulation |
Solution: AI-Powered Analysis combined with Python Validation
The solution combines AI-powered insights with deterministic validation to create a robust audit workflow:
1. Preliminary Analysis with Claude AI
- Data Upload: Ten years of financial statements were uploaded to Claude AI (Balance Sheets, P&Ls, Cash Flow Statements, and Key Financial Ratios), ensuring sensitive client data was redacted.
- M-Score Calculation and Risk categorization: Claude computed the eight ratios derived the M-Score using the formula:
M= -4.84 + 0.92×DSRI + 0.528×GMI + 0.404×AQI + 0.892×SGI + 0.115×DEPI - 0.172×SGAI + 4.679×TATA - 0.327×LVGI
- Risk Categorization: High-risk periods and anomalies were flagged for further investigation..
2. Validation with a Python Tool
- Automated Workflow: Built a simple python-based tool(with the help of ChatGPT)- to:
- Read Excel files with financial data.
- Independently compute ratios and M-Scores.
- Classify risk levels (High/Moderate/Low) and color-code results for easy interpretation.
- Key Features:
- Risk Visualization: HTML table with color-coded insights.
- Deterministic Validation: Ensured Claude’s AI insights aligned with Python’s calculations for maximum reliability.
Integration in Audit Workflow
- Step 1
- Create a clean, tabular summary of 10 years of client financials. Also prepare a separate Excel sheet focusing only on Beneish M-Score inputs.
- Step 2
Upload the file in PDF format to Claude AI and prompt it carefully:
- Calculate the Year wise Beneish-M score and generate interpretations with risk categorization, ensure that all parameters are clearly defined in the prompt including the formulas for each component.
- Identify Potential anomalies and red flags based on the 10 year financial statements, ensure that the specific areas to be analyzed like Key Financial trends and their relationships, Beneish Component red flags, supplementary forensic indicators etc. are listed out in the prompt.
- Step 3
Run the Python tool to validate the results of Beneish M Score. Investigate any mismatches and refine the analysis accordingly.
- Step 4
Prompt Claude AI to draft a structured audit plan focusing on areas and years flagged in the initial prompts. Ensure to specify the sections that are needed in the audit plan.
Note: Use ChatGPT or Claude to fine-tune your prompts for better output!.
Conclusion
This approach demonstrates how AI can truly transform forensic audits by:
- Accelerating Preliminary Analysis: Decades of data processed in minutes.
- Enhancing Accuracy: Python validation eliminates AI hallucination risks.
- Uncovering Hidden Risks: Component-level analysis reveals manipulation patterns missed by traditional methods.
For Chartered Accountants and forensic professionals, integrating AI with deterministic tools like Python offers a scalable, accurate, and strategic approach to forensic audits.
https://www.youtube.com/watch?v=qqR8QSGiXwY
