Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
49 Pages Posted: 8 Oct 2015 Last revised: 31 Oct 2019
Date Written: October 30, 2019
We develop a state-of-the-art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning method in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory-motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: Dechow et al.’s  logistic regression model based on financial ratios and Cecchini et al.’s  support-vector-machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.
Keywords: fraud prediction, machine learning, ensemble learning
JEL Classification: C53, M41
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