2010/05/18 by Mark Cecchini, Haldun Aytug, Haldun Aytuğ +3 · 367 citations
Business, Management and Accounting · Computer Science · Mathematics · #Artificial intelligence #Auditing, Earnings Management, Governance #Business #Computer science #Data mining #Data set #Domain (mathematical analysis) #Empirical research #Feature vector #Finance #Financial Distress and Bankruptcy Prediction #Financial management #Imbalanced Data Classification Techniques #Kernel (algebra) #Kernel method #Machine learning #Mathematics #Replicate #Statistics #Support vector machine
paper · doi:10.1287/mnsc.1100.1174
published in Management Science 56(7), 1146-1160 (Institute for Operations Research and the Management Sciences)
openalex publication_date 2010/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases the power of the learning machine by allowing an implicit and generally nonlinear mapping of points, usually into a higher dimensional feature space. A kernel specific to the domain of finance is developed. This financial kernel constructs features shown in prior research to be helpful in detecting management fraud. A large empirical data set was collected, which included quantitative financial attributes for fraudulent and nonfraudulent public companies. Support vector machines using the financial kernel correctly labeled 80% of the fraudulent cases and 90.6% of the nonfraudulent cases on a holdout set. Furthermore, we replicate other leading fraud research studies using our data and find that our method has the highest accuracy on fraudulent cases and competitive accuracy on nonfraudulent cases. The results validate the financial kernel together with support vector machines as a useful method for discriminating between fraudulent and nonfraudulent companies using only publicly available quantitative financial attributes. The results also show that the methodology has predictive value because, using only historical data, it was able to distinguish fraudulent from nonfraudulent companies in subsequent years.