Intro -- Bank Fraud -- Contents -- Preface -- Acknowledgments -- About the Author -- CHAPTER 1 Bank Fraud: Then and Now -- THE EVOLUTION OF FRAUD -- Fraud in the Present Day -- Risk and Reward -- Secured Lending versus Unsecured Lending -- Statistical Models and the Problem of Prediction -- THE EVOLUTION OF FRAUD ANALYSIS -- Early Credit Card Fraud -- Separating the Wheat from the Chaff -- The Advent of Nonlinear Statistical Models -- Tackling Fraud with Technology -- SUMMARY -- CHAPTER 2 Quantifying Fraud: Whose Loss Is It Anyway? -- Data Storage and Statistical Thinking -- Understanding Non-Fraud Behavior -- Quantifying Potential Risk -- Recording the Fraud Episode -- Supervised versus Unsupervised Modeling -- The Importance of Accurate Data -- FRAUD IN THE CREDIT CARD INDUSTRY -- Early Charge and Credit Cards -- Lost-and-Stolen Fraud: The Beginnings of Fraud in Credit Cards -- Card-Not-Present Fraud and Changes in the Marketplace -- THE ADVENT OF BEHAVIORAL MODELS -- FRAUD MANAGEMENT: AN EVOLVING CHALLENGE -- FRAUD DETECTION ACROSS DOMAINS -- USING FRAUD DETECTION EFFECTIVELY -- SUMMARY -- CHAPTER 3 In God We Trust. The Rest Bring Data! -- DATA ANALYSIS AND CAUSAL RELATIONSHIPS -- BEHAVIORAL MODELING IN FINANCIAL INSTITUTIONS -- Customer Expectations versus Standards of Privacy -- The Importance of Data in Implementing Good Behavioral Models -- SETTING UP A DATA ENVIRONMENT -- 1. Know Your Data -- 2. Collect All the Data You Can from Day One -- 3. Allow for Additions as the Data Grows -- 4. If You Cannot Integrate the Data, You Cannot Integrate the Businesses -- 5. When You Want to Change the Definition of a Field, It Is Best to Augment and Not Modify -- 6. Document the Data You Have as Well as the Data You Lost -- 7. When Change Happens, Document It -- 8. ETL: "Extract, Translate, Load" (Not "Extract, Taint, Lose").

9. A Data Model Is an Impressionist Painting -- 10. The Top Two Assets of Any Business Today Are People and Data -- UNDERSTANDING TEXT DATA -- SUMMARY -- CHAPTER 4 Tackling Fraud: The Ten Commandments -- 1. DATA: GARBAGE IN -- GARBAGE OUT -- 2. NO DOCUMENTATION? NO CHANGE! -- 3. KEY EMPLOYEES ARE NOT A SUBSTITUTE FOR GOOD DOCUMENTATION -- 4. RULES: MORE DOESN'T MEAN BETTER -- 5. SCORE: NEVER REST ON YOUR LAURELS -- 6. SCORE + RULES = WINNING STRATEGY -- 7. FRAUD: IT IS EVERYONE'S PROBLEM -- 8. CONTINUAL ASSESSMENT IS THE KEY -- 9. FRAUD CONTROL SYSTEMS: IF THEY REST, THEY RUST -- 10. CONTINUAL IMPROVEMENT: THE CYCLE NEVER ENDS -- SUMMARY -- CHAPTER 5 It Is Not Real Progress Until It Is Operational -- THE IMPORTANCE OF PRESENTING A SOLID PICTURE -- BUILDING AN EFFECTIVE MODEL -- 1. Operations Personnel Need to Understand the Concept of a Fraud Score -- 2. The Score Development Process Must Take into Consideration Operational Use and Constraints -- 3. In General, Fraud Strategies Should Complement and Not Compete with the Fraud Score -- 4. Fraud Strategies and Operational Processes Should Be Well Documented -- SUMMARY -- CHAPTER 6 The Chain Is Only as Strong as Its Weakest Link -- DISTINCT STAGES OF A DATA-DRIVEN FRAUD MANAGEMENT SYSTEM -- THE ESSENTIALS OF BUILDING A GOOD FRAUD MODEL -- Consortium-Based Models -- Deciding If the Juice Is Worth the Squeeze -- Monitoring and Fine-Tuning -- Implementing the Model Suite -- A GOOD FRAUD MANAGEMENT SYSTEM BEGINS WITH THE RIGHT ATTITUDE -- SUMMARY -- CHAPTER 7 Fraud Analytics: We Are Just Scratching the Surface -- A NOTE ABOUT THE DATA -- DATA -- Statistics -- REGRESSION 1 -- LOGISTIC REGRESSION 1 -- Neural Network 1 -- Regression 2 -- Logistic Regression 2 -- Neural Network 2 -- Regression 3 -- Logistic Regression 3 -- Neural Network 3 -- Regression 4 -- Logistic Regression 4 -- Neural Network 4.

"MODELS SHOULD BE AS SIMPLE AS POSSIBLE, BUT NOT SIMPLER" -- SUMMARY -- CHAPTER 8 The Proof of the Pudding May Not Be in the Eating -- UNDERSTANDING PRODUCTION FRAUD MODEL PERFORMANCE -- THE SCIENCE OF QUALITY CONTROL -- FALSE POSITIVE RATIOS -- MEASUREMENT OF FRAUD DETECTION AGAINST ACCOUNT FALSE POSITIVE RATIO -- UNSUPERVISED AND SEMISUPERVISED MODELING METHODOLOGIES -- SUMMARY -- CHAPTER 9 The End: It Is Really the Beginning! -- Notes -- Index.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.