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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
Acknowledgments
About the Author
Chapter One Bank Fraud: Then and Now
The Evolution of Fraud
The Evolution of Fraud Analysis
Summary
Chapter Two Quantifying Fraud: Whose Loss is it Anyways?
Fraud in the Credit Card Industry
The Advent of Behavioral Models
Fraud Management: An Evolving Challenge
Fraud Detection across Domains
Using Fraud Detection Effectively
Summary
Chapter Three In God We Trust; the Rest Bring Data!
Data Analysis and Causal Relationships
Behavioral Modeling in Financial Institutions
Setting Up a Data Environment
Understanding Text Data
Summary
Chapter Four 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 Five It Is Not Real Progress Until It Is Operational
The Importance of Presenting a Solid Picture
Building an Effective Model
Summary
Chapter Six The Chain is as Strong as the Weakest Link
Distinct Stages of a Data Driven Fraud Management System
The Essentials of Building a Good Fraud Model
A Good Fraud Management System Begins with the Right Attitude
Summary
Chapter Seven Fraud Analytics: We Are Just Scratching the Surface
A Note about the Data
Data
Regression 1
Logistic Regression 1
“Models Should Be As Simple As Possible, But Not More So”
Summary
Chapter Eight The Proof of the Pudding May Not Be in the Eating
The Science of Quality Control
False Positive Ratios
Measurement of Fraud Detection Against AFPR
Unsupervised and Semi-supervised Modeling Methodologies
Summary
Chapter Nine The End: It Is Really the Beginning!
Notes
Index
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