Machine Learning Methods For Detecting Patterns Of Management Fraud

Authors:
David G. Whiting;James V. Hansen;James B. McDonald;Conan Albrecht;W. Steve Albrecht
Affiliations:
Department of Statistics, Brigham Young University, Provo, Utah, USA;Department of Information Systems, Marriott School of Management, Brigham Young University, Provo, Utah, USA;Department of Economics, Brigham Young University, Provo, Utah, USA;Department of Information Systems, Marriott School of Management, Brigham Young University, Provo, Utah, USA;School of Accountancy, Marriott School of Management Brigham Young University, Provo, Utah, USA
Venue:
Computational Intelligence
Year:
2012

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Abstract

Discovery of financial fraud has profound social consequences. Loss of stockholder value, bankruptcy, and loss of confidence in the professional audit firms have resulted from failure to detect financial fraud. Previous studies that have attempted to discover fraud patterns from publicly available information have achieved only moderate levels of success. This study explores the capabilities of recently developed statistical learning and data mining methods in an attempt to advance fraud discovery performance to levels that have potential for proactive discovery or mitigation of financial fraud. The partially adaptive methods we test have achieved success in a number of complex problem domains and are easily interpretable. Ensemble methods, which combine predictions from multiple models via boosting, bagging, or related approaches, have emerged as among the most powerful data mining and machine learning methods. Our study includes random forests, stochastic gradient boosting, and rule ensembles. The results for ensemble models show marked improvement over past efforts, with accuracy approaching levels of practical potential. In particular, rule ensembles do so while maintaining a degree of interpretability absent in the other ensemble methods. © 2012 Wiley Periodicals, Inc.