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Bank Fraud: Using Technology to Combat Losses

Bank Fraud: Using Technology to Combat Losses

  • 作者:
  • 出版商: John Wiley & Sons
  • ISBN: 9780470494394
  • 出版时间 April 2014
  • 规格: Hardback , 192 pages
  • 适应领域: International ? 免责申明:
    Countri(es) stated herein are used as reference only

List Price: ¥418.50

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  • 描述 
  • 大纲 
  • 作者 
  • 详细

    Learn how advances in technology can help curb bank fraud

    Fraud prevention specialists are grappling with ever-mounting quantities of data, but in today's volatile commercial environment, paying attention to that data is more important than ever. Bank Fraud provides a frank discussion of the attitudes, strategies, and—most importantly—the technology that specialists will need to combat fraud.

    Fraudulent activity may have increased over the years, but so has the field of data science and the results that can be achieved by applying the right principles, a necessary tool today for financial institutions to protect themselves and their clientele. This resource helps professionals in the financial services industry make the most of data intelligence and uncovers the applicable methods to strengthening defenses against fraudulent behavior. This in-depth treatment of the topic begins with a brief history of fraud detection in banking and definitions of key terms, then discusses the benefits of technology, data sharing, and analysis, along with other in-depth information, including:

    • The challenges of fraud detection in a financial services environment
    • The use of statistics, including effective ways to measure losses per account and ROI by product/initiative
    • The Ten Commandments for tackling fraud and ways to build an effective model for fraud management

    Bank Fraud offers a compelling narrative that ultimately urges security and fraud prevention professionals to make the most of the data they have so painstakingly gathered. Such professionals shouldn't let their most important intellectual asset—data—go to waste. This book shows you just how to leverage data and the most up-to-date tools, technologies, and methods to thwart fraud at every turn.

  • Preface xi

    Acknowledgments xiii

    About the Author xvii

    Chapter 1 Bank Fraud: Then and Now 1

    The Evolution of Fraud 2

    The Evolution of Fraud Analysis 8

    Summary 14

    Chapter 2 Quantifying Fraud: Whose Loss Is It Anyway? 15

    Fraud in the Credit Card Industry 22

    The Advent of Behavioral Models 30

    Fraud Management: An Evolving Challenge 31

    Fraud Detection across Domains 33

    Using Fraud Detection Effectively 35

    Summary 37

    Chapter 3 In God We Trust. The Rest Bring Data! 39

    Data Analysis and Causal Relationships 40

    Behavioral Modeling in Financial Institutions 42

    Setting Up a Data Environment 47

    Understanding Text Data 58

    Summary 60

    Chapter 4 Tackling Fraud: The Ten Commandments 63

    1. Data: Garbage In; Garbage Out 67

    2. No Documentation? No Change! 71

    3. Key Employees Are Not a Substitute for Good Documentation 75

    4. Rules: More Doesn’t Mean Better 77

    5. Score: Never Rest on Your Laurels 79

    6. Score + Rules = Winning Strategy 83

    7. Fraud: It Is Everyone’s Problem 85

    8. Continual Assessment Is the Key 86

    9. Fraud Control Systems: If They Rest, They Rust 87

    10. Continual Improvement: The Cycle Never Ends 88

    Summary 88

    Chapter 5 It Is Not Real Progress Until It Is Operational 89

    The Importance of Presenting a Solid Picture 90

    Building an Effective Model 92

    Summary 105

    Chapter 6 The Chain Is Only as Strong as Its Weakest Link 109

    Distinct Stages of a Data-Driven Fraud Management System 110

    The Essentials of Building a Good Fraud Model 112

    A Good Fraud Management System Begins with the Right Attitude 117

    Summary 119

    Chapter 7 Fraud Analytics: We Are Just Scratching the Surface 121

    A Note about the Data 125

    Data 126

    Regression 1 128

    Logistic Regression 1 132

    “Models Should Be as Simple as Possible, But Not Simpler” 149

    Summary 151

    Chapter 8 The Proof of the Pudding May Not Be in the Eating 153

    Understanding Production Fraud Model Performance 154

    The Science of Quality Control 155

    False Positive Ratios 156

    Measurement of Fraud Detection against Account False Positive Ratio 156

    Unsupervised and Semisupervised Modeling Methodologies 158

    Summary 159

    Chapter 9 The End: It Is Really the Beginning! 161

    Notes 165

    Index 167

  • Revathi Subramanian is Senior Vice President, Data Science at CA Technologies, which helps Fortune 1000 companies manage and secure complex IT environments to support agile business services. She is the founding member of a team of high caliber data scientists that are uncovering business value and operational intelligence from the chaos of Big Data in areas like eCommerce, application performance management, infrastructure management, service virtualization, and project management. Before joining CA, Revathi was the co-founder of the SAS Advanced Analytic Solutions Division in 2002. She led the development of a new enterprise real-time fraud decisioning platform utilizing advanced analytics. Revathi has a Master’s degree in Statistics from The Ohio State University and a Bachelor’s degree in Mathematics from Ethiraj Collge, Chennai, India.

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