A Machine Learning Approach to Detect Accounting Frauds

This paper introduces a new fraud detection model to the accounting literature using machine learning (ML). This model, which we refer to as LogitBoost, applies ensemble learning to logistic regressions. We show, using seven alternative measures assessing the ability to detect fraud, that our model outperforms the methods based solely on logistic regressions or other ML methods used by prior literature. Additionally, our model outperforms the others in predicting fraud beyond the current accounting period. Importantly, our method relies on a lower number of predictors than those used in prior ML research, thus minimizing concerns over multicollinearity and potential overfitting associated with machine learning methods.

Keywords: machine learning, logistic regressions, accounting irregularities, AAERs

JEL Classification: C44, C50, C53, M41

Suggested Citation: Suggested Citation

Hassanniakalager, Arman and Perotti, Pietro and Tsoligkas, Fanis, A Machine Learning Approach to Detect Accounting Frauds (December 30, 2022). Available at SSRN: https://ssrn.com/abstract=4117764 or http://dx.doi.org/10.2139/ssrn.4117764