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Machine Learning Advances in Payment Card Fraud Detection provides a thorough review of the state-of-the-art in fraud detection research that is ideal for graduate-level readers and professionals. Through a comprehensive examination of fraud analytics that covers data collection, steps for cleaning and processing data, tools for analysing data, and ways to draw insights, the book argues for a new direction to be taken in developing state-of the-art payment fraud detection techniques. It uses an extensive analysis and description of an exemplar fraud detection algorithm, SOAR, to illustrate how a detailed understanding of the payment fraud domain can be used to motivate further advances in fraud detection techniques. The book concludes with a discussion of opportunities for future research, such as developing holistic approaches for countering fraud.
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Machine Learning Advances in Payment Card Fraud Detection provides a thorough review of the state-of-the-art in fraud detection research that is ideal for graduate-level readers and professionals. Through a comprehensive examination of fraud analytics that covers data collection, steps for cleaning and processing data, tools for analysing data, and ways to draw insights, the book argues for a new direction to be taken in developing state-of the-art payment fraud detection techniques. It uses an extensive analysis and description of an exemplar fraud detection algorithm, SOAR, to illustrate how a detailed understanding of the payment fraud domain can be used to motivate further advances in fraud detection techniques. The book concludes with a discussion of opportunities for future research, such as developing holistic approaches for countering fraud.