Accounting fraud is a worldwide problem with potentially serious consequences, but it is often detected after the damage has been done. Hence, efficient and effective methods of detecting corporate accounting fraud would offer significant value to regulators, auditors, and investors.
In a new study, we develop a state-of-the-art fraud prediction model using a machine learning. Following prior research in accounting fraud detection, we use as our sample the detected material accounting misstatements disclosed in the SEC’s Accounting and Auditing Enforcement Releases (AAERs). Our sample covers all publicly listed U.S. firms over the period 1991–2008. Although there are useful nonfinancial predictors … Read more