Neural network detection of management fraud using published financial data

TLDR

Research on management fraud detection is sparse compared to bankruptcy prediction. The study aims to develop an ANN model for detecting management fraud and to examine publicly available predictors of fraudulent financial statements. Using a self‑organizing ANN AutoNet and standard statistical tools, the authors test publicly available predictors in a matched‑pairs sample to build a fraud detection model. The resulting model achieves high detection probability, confirming AutoNet’s validity and supporting the identified red‑flag predictors.

Abstract

This paper uses Artificial Neural Networks to develop a model for detecting management fraud. Although similar to the more widely investigated area of bankruptcy prediction, research has been minimal. To increase the body of knowledge on this subject, we offer an in-depth examination of important publicly available predictors of fraudulent financial statements. We test the value of these suggested variables for detection of fraudulent financial statements within a matched pairs sample. We use a self organizing Artificial Neural Network (ANN) AutoNet in conjunction with standard statistical tools to investigate the usefulness of these publicly available predictors. Our study results in a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements.