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.

• Preface xiAcknowledgments xiiiAbout the Author xviiChapter 1 Bank Fraud: Then and Now 1The Evolution of Fraud 2The Evolution of Fraud Analysis 8Summary 14Chapter 2 Quantifying Fraud: Whose Loss Is It Anyway? 15Fraud in the Credit Card Industry 22The Advent of Behavioral Models 30Fraud Management: An Evolving Challenge 31Fraud Detection across Domains 33Using Fraud Detection Effectively 35Summary 37Chapter 3 In God We Trust. The Rest Bring Data! 39Data Analysis and Causal Relationships 40Behavioral Modeling in Financial Institutions 42Setting Up a Data Environment 47Understanding Text Data 58Summary 60Chapter 4 Tackling Fraud: The Ten Commandments 631. Data: Garbage In; Garbage Out 672. No Documentation? No Change! 713. Key Employees Are Not a Substitute for Good Documentation 754. Rules: More Doesn’t Mean Better 775. Score: Never Rest on Your Laurels 796. Score + Rules = Winning Strategy 837. Fraud: It Is Everyone’s Problem 858. Continual Assessment Is the Key 869. Fraud Control Systems: If They Rest, They Rust 8710. Continual Improvement: The Cycle Never Ends 88Summary 88Chapter 5 It Is Not Real Progress Until It Is Operational 89The Importance of Presenting a Solid Picture 90Building an Effective Model 92Summary 105Chapter 6 The Chain Is Only as Strong as Its Weakest Link 109Distinct Stages of a Data-Driven Fraud Management System 110The Essentials of Building a Good Fraud Model 112A Good Fraud Management System Begins with the Right Attitude 117Summary 119Chapter 7 Fraud Analytics: We Are Just Scratching the Surface 121A Note about the Data 125Data 126Regression 1 128Logistic Regression 1 132“Models Should Be as Simple as Possible, But Not Simpler” 149Summary 151Chapter 8 The Proof of the Pudding May Not Be in the Eating 153Understanding Production Fraud Model Performance 154The Science of Quality Control 155False Positive Ratios 156Measurement of Fraud Detection against Account False Positive Ratio 156Unsupervised and Semisupervised Modeling Methodologies 158Summary 159Chapter 9 The End: It Is Really the Beginning! 161Notes 165Index 167

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