Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach

ABSTRACT 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 methods 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…

• NN
  National Natural Science Foundation of China

  Awards: 71971164, 91646206, 71601116