{"product_id":"bank-fraud-isbn-9780470494394","title":"Bank Fraud","description":"\u003cb\u003eLearn how advances in technology can help curb bank fraud\u003c\/b\u003e  \u003cp\u003eFraud 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. \u003ci\u003eBank Fraud\u003c\/i\u003e provides a frank discussion of the attitudes, strategies, and—most importantly—the technology that specialists will need to combat fraud.\u003c\/p\u003e \u003cp\u003eFraudulent 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe challenges of fraud detection in a financial services environment\u003c\/li\u003e \u003cli\u003eThe use of statistics, including effective ways to measure losses per account and ROI by product\/initiative\u003c\/li\u003e \u003cli\u003eThe Ten Commandments for tackling fraud and ways to build an effective model for fraud management\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBank Fraud\u003c\/i\u003e 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.\u003c\/p\u003e  \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eAbout the Author xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Bank Fraud: Then and Now 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Evolution of Fraud 2\u003c\/p\u003e \u003cp\u003eThe Evolution of Fraud Analysis 8\u003c\/p\u003e \u003cp\u003eSummary 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Quantifying Fraud: Whose Loss Is It Anyway? 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFraud in the Credit Card Industry 22\u003c\/p\u003e \u003cp\u003eThe Advent of Behavioral Models 30\u003c\/p\u003e \u003cp\u003eFraud Management: An Evolving Challenge 31\u003c\/p\u003e \u003cp\u003eFraud Detection across Domains 33\u003c\/p\u003e \u003cp\u003eUsing Fraud Detection Effectively 35\u003c\/p\u003e \u003cp\u003eSummary 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 In God We Trust. The Rest Bring Data! 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Analysis and Causal Relationships 40\u003c\/p\u003e \u003cp\u003eBehavioral Modeling in Financial Institutions 42\u003c\/p\u003e \u003cp\u003eSetting Up a Data Environment 47\u003c\/p\u003e \u003cp\u003eUnderstanding Text Data 58\u003c\/p\u003e \u003cp\u003eSummary 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Tackling Fraud: The Ten Commandments 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Data: Garbage In; Garbage Out 67\u003c\/p\u003e \u003cp\u003e2. No Documentation? No Change! 71\u003c\/p\u003e \u003cp\u003e3. Key Employees Are Not a Substitute for Good Documentation 75\u003c\/p\u003e \u003cp\u003e4. Rules: More Doesn’t Mean Better 77\u003c\/p\u003e \u003cp\u003e5. Score: Never Rest on Your Laurels 79\u003c\/p\u003e \u003cp\u003e6. Score + Rules = Winning Strategy 83\u003c\/p\u003e \u003cp\u003e7. Fraud: It Is Everyone’s Problem 85\u003c\/p\u003e \u003cp\u003e8. Continual Assessment Is the Key 86\u003c\/p\u003e \u003cp\u003e9. Fraud Control Systems: If They Rest, They Rust 87\u003c\/p\u003e \u003cp\u003e10. Continual Improvement: The Cycle Never Ends 88\u003c\/p\u003e \u003cp\u003eSummary 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 It Is Not Real Progress Until It Is Operational 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Importance of Presenting a Solid Picture 90\u003c\/p\u003e \u003cp\u003eBuilding an Effective Model 92\u003c\/p\u003e \u003cp\u003eSummary 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 The Chain Is Only as Strong as Its Weakest Link 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDistinct Stages of a Data-Driven Fraud Management System 110\u003c\/p\u003e \u003cp\u003eThe Essentials of Building a Good Fraud Model 112\u003c\/p\u003e \u003cp\u003eA Good Fraud Management System Begins with the Right Attitude 117\u003c\/p\u003e \u003cp\u003eSummary 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Fraud Analytics: We Are Just Scratching the Surface 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Note about the Data 125\u003c\/p\u003e \u003cp\u003eData 126\u003c\/p\u003e \u003cp\u003eRegression 1 128\u003c\/p\u003e \u003cp\u003eLogistic Regression 1 132\u003c\/p\u003e \u003cp\u003e“Models Should Be as Simple as Possible, But Not Simpler” 149\u003c\/p\u003e \u003cp\u003eSummary 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 The Proof of the Pudding May Not Be in the Eating 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Production Fraud Model Performance 154\u003c\/p\u003e \u003cp\u003eThe Science of Quality Control 155\u003c\/p\u003e \u003cp\u003eFalse Positive Ratios 156\u003c\/p\u003e \u003cp\u003eMeasurement of Fraud Detection against Account False Positive Ratio 156\u003c\/p\u003e \u003cp\u003eUnsupervised and Semisupervised Modeling Methodologies 158\u003c\/p\u003e \u003cp\u003eSummary 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The End: It Is Really the Beginning! 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNotes 165\u003c\/p\u003e \u003cp\u003eIndex 167\u003c\/p\u003e  \u003cp\u003e\"... you come away from the book feeling enriched, and enthused....\" (\u003cem\u003eProfessional Security,\u003c\/em\u003e July 2014)   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRevathi Subramanian\u003c\/b\u003e 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.\u003c\/p\u003e   \u003cp\u003eBANK \u003cb\u003eFRAUD\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eUsing Technology to Combat Losses\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eGreat strides in data intelligence have been made over the years as the fraud detection and prevention industry has matured. With that comes a need for technology that can handle all of this data, as well as people who know how to correctly use it. As part of the Wiley and SAS Business Series, \u003ci\u003eBank Fraud: Using Technology to Combat Losses\u003c\/i\u003e dives deep into fraud detection and prevention strategies from a technological perspective. The book is aimed at helping users define their data and analysis environments correctly from the beginning, so that the best possible results can be achieved by their fraud management systems. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBank Fraud\u003c\/i\u003e is not meant to convert the reader into a data scientist, but rather aims to convert the reader into a power user of data-driven systems while presenting some key aspects of a good fraud solution. It covers the history of fraud detection and prevention along with practical tools for understanding risk exposure, key terms, statistics, and trends. It also discusses the special vulnerability banks have when it comes to fraud and the historical challenges in locating perpetrators. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBank Fraud\u003c\/i\u003e provides guidance for loss prevention professionals to assess which technology is appropriate for battling bank fraud and how to properly implement it. Its advice is timely and relevant, as combating fraud is listed as a top priority for almost every bank in existence today. Readers get a look at fraud prevention from an expert's perspective and learn to use technology as a harness for data intelligence – effectively stopping fraudulent activity in its tracks.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988787478757,"sku":"NP9780470494394","price":45.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470494394.jpg?v=1761781591","url":"https:\/\/k12savings.com\/es\/products\/bank-fraud-isbn-9780470494394","provider":"K12savings","version":"1.0","type":"link"}