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.management fraud, classification, support vector machines, financial event detection, kernel methods

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We examine whether initial public offering (IPO) firms exercise discretion over an individual accrual account on the balance sheet-the allowance for uncollectible accounts-and an individual accrual account on the income statement-bad debt expense. Our research design exploits a unique disclosure requirement related to these accounts (i.e., the ex post disclosure of write-offs of uncollectible accounts), which enables us to develop refined expectation models. We provide evidence that IPO firms have conservative, not aggressive, allowances in the annual periods adjacent to their stock offerings. In fact, the average IPO firm has an allowance that is over four-times leading write-offs. We also provide evidence that IPO firms record larger, not smaller, bad debt expense and are less likely to record income-increasing bad debt expense than matched non-IPO firms. These results challenge the view that IPO firms understate receivables-related accrual accounts.Keywords Initial public offering Á Earnings management Á Allowance for uncollectible accounts Á Bad debt expense