Financial Statement Fraud

Auditing: A Journal of Practice & Theory Vol. 30, No. 2 May 2011 pp. 19–50

American Accounting Association DOI: 10.2308/ajpt-50009

Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms
Johan Perols
SUMMARY: This study compares the performance of six popular statistical and machine learning models in detecting financial statement fraud under different assumptions of misclassification costs and ratios of fraud firms to nonfraud firms. The results show, somewhat surprisingly, that logistic regression and support vector machines perform well relative to an artificial neural network, bagging, C4.5, and stacking. The results also reveal some diversity in predictors used across the classification algorithms. Out of 42 predictors examined, only six are consistently selected and used by different classification algorithms: auditor turnover, total discretionary accruals, Big 4 auditor, accounts receivable, meeting or beating analyst forecasts, and unexpected employee productivity. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models. Keywords: analytical auditing; financial statement fraud; fraud detection; fraud predictors; classification algorithms. Data Availability: A list of fraud companies used in this study is available from the author upon request. All other data sources are described in the text.

INTRODUCTION

T

he cost of financial statement fraud is estimated at $572 billion1 per year in the U.S. (Association of Certified Fraud Examiners [ACFE] 2008). In addition to direct costs, financial statement fraud negatively affects employees and investors and undermines the

Johan Perols is an Assistant Professor at the University of San Diego.
This study is based on one of my three dissertation papers completed at the University of South Florida. I thank my dissertation co-chairs, Jacqueline Reck and Kaushal Chari, and committee

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