Classification techniques for the identification of falsified financial statements: a comparative analysis

Abstract In this study, I develop 10 alternative classification models using logit analysis, discriminant analysis, support vector machines, artificial neural networks, probabilistic neural networks, nearest neighbours, UTADIS and MHDIS for the detection of falsified financial statements. The models are developed using financial and nonfinancial data. The sample includes 398 financial statements, half of which were assigned a qualified audit opinion. I compare these alternatives methods using out‐of‐time and out‐of‐sample tests. The results are used to derive conclusions on the performance of the methods and to investigate the potential of developing models that will assist auditors in identifying fraudulent financial statements. Copyright © 2009 John Wiley & Sons, Ltd.