Machine Learning Advances in Payment Card Fraud Detection

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Language: English
Cover of the book Machine Learning Advances in Payment Card Fraud Detection

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350 p. · 15.2x22.9 cm · Paperback

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 analyzing data, and ways to draw insights, the book introduces state-of the-art payment fraud detection techniques. Other topics covered include machine learning techniques for the detection of fraud, including SOAR, and opportunities for future research, such as developing holistic approaches for countering fraud.



  • Covers analytical approaches and machine learning for fraud detection
  • Explores SOAR with full R-code and example obfuscated datasets in a freely-accessible companion website
  • Introduces state-of the-art payment fraud detection techniques

1. History of Payment Cards, Payment Fraud Prevention and Detection Technologies 2. Analytical Approaches to Fraud Detection and Understanding 3. Disruptive Payment Technologies and the Pivotal Event 4. Machine Learning for Fraud Detection 5. SOAR: A Tool to Transparently Explain Patterns of Fraud 6. The Future

Graduate level (MBA) and professionals working in credit card fraud detection and analysis

Nick F. Ryman-Tubb helped to pioneer the application of artificial intelligence (AI) and deep learning neural networks within the financial industry. In 1986, he founded Neural Technologies in the UK, among the first AI businesses focused on risk, banking, and payment fraud. After his exit in 2000, Nick joined businesses that today deploy his AI in insurance, money laundering, contactless/mobile payment fraud detection, protecting over 150 institutions, more than 3 million merchants, 1 billion cards, and over 30 billion credit/debit card transactions a year. Nick is a professor and Machine Learning Impresario at the University of Surrey where he teaches and continues his research. He recently formed the Institute for Financial Innovation in Transactions and Security (FITS) as a non-profit organisation with a simple vision - to dramatically reduce payment fraud using AI. Today, FITS works with all its industry members towards this shared vision.
Paul Krause is professor in Complex Systems at the University of Surrey. He graduated in pure mathematics and experimental physics from the University of Exeter, and then spent the next ten years as a researcher in geophysics and then low-temperature physics. Following that, he moved to his main research career path in the theory and practice of AI. While maintaining, with a spirited defence, that we are far, far off any “AI singularity”, he does believe that the computer age is providing us with a set of valuable tools to help us achieve a better understanding of the intensely interdisciplinary problems that complexity science is now beginning to address.