Machine Learning Advances in Payment Card Fraud Detection
Book
Paperback/Softback
350 pages
978-0-12-813415-3 (ISBN)
Description
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.
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.
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-0-12-813415-3 (9780128134153)
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Schweitzer Classification
Persons
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.
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.
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.
Content
1. Introduction
2. History of payment cards
3. Growth of payment card fraud
4. The fraud detection problem
5. Understanding the fraud domain
6. Cost of payment fraud
7. History of payment card fraud detection methods
8. The pivotal event and disruptive technologies
9. The Sparse Oracle-based Adaptive Rule extraction algorithm
10. Real-world data empirical evaluation
11. Discussion and conclusion
2. History of payment cards
3. Growth of payment card fraud
4. The fraud detection problem
5. Understanding the fraud domain
6. Cost of payment fraud
7. History of payment card fraud detection methods
8. The pivotal event and disruptive technologies
9. The Sparse Oracle-based Adaptive Rule extraction algorithm
10. Real-world data empirical evaluation
11. Discussion and conclusion