Detecting Accounting Fraud in Global Corporations: An Audit-Based Analysis Using Benford's Law

This paper focuses on the problem of Benford Law as the effective statistical method which can help to discover the presence of accounting anomalies among large non-banking MNCs. The data used to perform the study are the initial decimal place distributions of six financial variables using 593 financial items with data retrieved through SEC 10-K filings of 20 global companies over five fiscal years (20202024) namely Revenue, Cost of Goods Sold (COGS), Operating Expenses, Total Assets, Net Income, and Accounts Receivable. These findings suggest that there are statistically significant departures of the data in terms of the distribution of Benford, achieving the chi-square value of 23.642 and the Mean Absolute Deviation (MAD) of 0.0195 highly exceed the critical value of 15.507 and 0.015, respectively. COGS and Total Assets have the most significant degrees of deviation, whereas Accounts Receivable has the least number of deviations. Sectoral analysis shows that the firms in the energy sector have the highest structural divergence; this is associated majorly with the volatility of the commodity prices. Moreover, there was also a steady increase in conformity levels in 2021-24, which can be attributed to post-pandemic regulatory changes. The research findings are that Benford Law although it is a good preliminary audit screening can never be utilized alone as evidence of fraud. When used in conjunction with more general forensic accounting methods, its usefulness improves greatly.