+353-1-416-8900REST OF WORLD
1-800-526-8630U.S. (TOLL FREE)

Bank Fraud. Using Technology to Combat Losses. Wiley and SAS Business Series

  • ID: 2219908
  • Book
  • May 2014
  • 192 Pages
  • John Wiley and Sons Ltd
1 of 5

Using the right technology to battle widespread financial fraud

Data intelligence has evolved over the years, resulting in highly sophisticated scoring processes. Bank Fraud: Using Technology to Combat Losses teaches loss prevention managers, fraud prevention professionals, and corporate security personnel how to effectively select and use the right technology to combat fraudulent activities in their business. This book covers in detail all of the ingredients necessary to build and maintain a healthy fraud management environment, including:

  • A discussion of the history of fraud detection and prevention practices
  • The challenges of fraud detection in a financial services environment
  • Corporate risk exposure and setting up a solid data environment
  • A discussion of exposure considerations and how to avoid losses
  • Statistical analysis and evaluating trends over time

Data–driven risk management goes back decades, but many professionals simply miscalculated their strategies or failed to plan them adequately. Bank Fraud examines the current technology to teach professionals how to properly plan, implement, and evaluate their loss prevention systems and find modern solutions for age–old fraudulent activity. It is a new take on finding the right data environment for the business and applying it correctly to ensure the best security results over time.

Note: Product cover images may vary from those shown
2 of 5


3 of 5

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

Note: Product cover images may vary from those shown
4 of 5

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.

Note: Product cover images may vary from those shown
5 of 5
Note: Product cover images may vary from those shown