Bethany Hoogs

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This paper considers an application of evolutionary computation (EC) to classification and pattern discovery. In particular we present a genetic algorithm (GA) utilized to discriminate cases of potential financial statement fraud. Of key interest to us is the ability to distinguish multidimensional patterns over time. The GA evolves strings over a pattern(More)
Automatic detection of anomalies in financial statements can decrease the risk of exposure to fraudulent corporate behavior. This paper proposes a method to convert fraud classification rules learned from a genetic algorithm to a fuzzy score representing the degree to which a company’s financial statements match those rules. Applying the method to financial(More)
Sales representatives must have access to meaningful and actionable intelligence about potential customers to be effective in their roles. Historically, GE Capital Americas sales reps identified leads by manually searching through news reports and financial statements either in print or online. Here we describe a system built to automate the collection and(More)
Traditional sales support systems offer an insular view of companies, providing information on a target company and that company alone. This view presents each company as if it operates independently from its surroundings. However, a company can be more effectively evaluated when it is viewed not in isolation, but in the context of its network of customers(More)
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