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Along with the rise of credit card use, fraud is on the rise. To address fraud, financial institutions (FIs) are employing fraud detection systems (FDS),however, the majority of cases being flagged by this system are False Positives. The possibilities of enhancing the current operation by post processing the FDS output constitute the objective of this book. The data used for the analysis was provided by one of the major Canadian banks. Based on several variations and combinations of features and training class distributions, different models(more than seventy) were developed to explore the influence of these parameters on the performance of the desired system. The results indicate that the employed approach and the prototype developed have a very good potential to improve on the existing system leading to significant savings for the FIs. This is a very well written book and could be useful and of interest to academia in general and Computer, AI & Information Technology applications, in particular. This book could also be useful to professionals in FIS and banking industry or any individual who may be interested in the real world applications of AI and IT.

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Soheila Ehramikar: Obtained M.Sc. in Engineering with main focus on Business & Management of Technology from the University of Toronto; Obtained TCM Degree from Canada, R.Ac., Current practice: Holistic Medicine, Yoga Teacher and Public Speaker, Toronto, Canada; She travels extensively and has resided in several culturally diverse countries.

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