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This chapter explores statistical methods that may be used for screening for corporate fraud and earnings manipulation. It briefly discusses time series analysis, cross-sectional analysis and TSCS analysis for corporate fraud detection. Then the chapter details practical models like the Beneish M-Score, Montier C-score, Sloan Accruals Ratio, etc., explaining their calculations and interpretations. It also introduces Benford’s Law, a principle used to detect anomalies in financial data, and the AB-Score and ABF-Score, which build upon Benford’s Law and the Dechow F-Score. Finally, the chapter touches briefly on other statistical tools like the Z-Score, Chi-square test, Runs test, and multivariate analysis as building blocks for corporate fraud detection.

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Using Statistical Methods to Screen for Earnings Manipulation and Corporate Fraud

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  • 发表日期：

    2025年

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  • Jesper Sørensen

摘要

This chapter explores statistical methods that may be used for screening for corporate fraud and earnings manipulation. It briefly discusses time series analysis, cross-sectional analysis and TSCS analysis for corporate fraud detection. Then the chapter details practical models like the Beneish M-Score, Montier C-score, Sloan Accruals Ratio, etc., explaining their calculations and interpretations. It also introduces Benford’s Law, a principle used to detect anomalies in financial data, and the AB-Score and ABF-Score, which build upon Benford’s Law and the Dechow F-Score. Finally, the chapter touches briefly on other statistical tools like the Z-Score, Chi-square test, Runs test, and multivariate analysis as building blocks for corporate fraud detection.