Bank Fraud Using Technology to Combat Losses

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

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.