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Corporate fraud : the human factor
\"Drawing on the practical experiences of fraud investigators from across the world, in this book we provide perspectives to help you identify the many guises of the \"fraud trail\" - taking into account cultural, technological and social factors and our predictions for the future. We consider the impact of factors as diverse as technological evolution, changing demographics and where 'following the money' is likely to lead in the future. It is through stories of ordinary and extraordinary frauds and fraudsters and practical experiences of those that have investigated them, that we provide a 'fraud lens' to spot the warning signs before a small transgression becomes a huge fraud which could threaten the future of an organisation.\" -- dust jacket
Book
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud
Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.
Journal Article
