Detecting Management Fraud in Public Companies

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Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “See Carrizosa and Romero Morales (2013) for a recent review on Mathematical Optimization and the SVM, and Bertsimas et al. (2008), Brooks (2011), Cecchini et al. (2010), Chaovalitwongse et al. (2008), Guyon et al. (2002), Martens et al. (2007), Romero Morales and Wang (2010) for successful…”
Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms (2011, unknown): “Future research can also follow Cecchini et al. (2010) and develop artifacts that are designed speciﬁcally for the fraud domain.”
Corporate residence fraud detection (2014, background): “For the purpose of tax evasion, data mining has been applied to the problem of corporate fraud [7, 20], where companies falsify their financial statements, as well as Value Added Tax (VAT) evasion [2, 16, 41], solely on structured data.”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, background): “…of False Negatives Total Error Cost Per Instance Savings vs. Audit All Savings vs. Audit None MetaFraud $40.0 $60.6 $100.5 $307.2 $225.5 Cecchini et al. 2010 $82.2 $67.0 $149.2 $258.6 $176.8 Kirkos et al. 2007 - BayesNet $112.5 $118.8 $231.2 $176.5 $94.8 Kirkos et al. 2007 – ID3 $58.3…”