|Item type||Location||Collection||Call number||Copy number||Status||Date due|
|Text||Reserve Section||Non Fiction||332.10684 SUB 2014 (Browse shelf)||C-1||Not For Loan|
|Text||Circulation Section||Non Fiction||332.10684 SUB 2014 (Browse shelf)||C-2||Available|
Includes bibliographical references and index.
Table of contents 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
"Capitalize on technology to halt bank fraudExamining the technology that is needed to combat bank fraud, Bank Fraud: Using Technology to Combat Losses equips corporate security and loss prevention managers with the necessary tools to determine an organization's unique technology needs. Looks at the technology needed to handle data intelligence Provides guidance to assess the technology necessary to battle fraud Features unique coverage of the history of fraud detection and prevention in banking Explores the challenges of fraud detection in a financial services environment; understanding corporate risk exposure; losses per assets; trending over time; benefits of technology Focusing on the financial crimes and insider frauds in operation nationally and internationally, Bank Fraud: Using Technology to Combat Losses arms fraud prevention professionals with authoritative guidance to detect and prevent such crimes in future"--