Revathi Subramanian

Bank Fraud (eBook, PDF)

Using Technology to Combat Losses

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 192
• Erscheinungstermin: 12. März 2014
• Englisch
• ISBN-13: 9781118220320
• Artikelnr.: 40612669

Autorenporträt

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.

Inhaltsangabe

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