Bank Fraud : Using Technology to Combat Losses

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 4/14/2014
  • Publisher: Wiley
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Capitalize on technology to halt bank fraud Examining the technology that is needed to combat bank fraud, Bank Fraud: Using Technology to Combat Losses equips corporate security and loss prevention managers with the necessary tools to determine an organization's unique technology needs. Looks at the technology needed to handle data intelligence Provides guidance to assess the technology necessary to battle fraud Features unique coverage of the history of fraud detection and prevention in banking Explores the challenges of fraud detection in a financial services environment; understanding corporate risk exposure; losses per assets; trending over time; benefits of technology Focusing on the financial crimes and insider frauds in operation nationally and internationally, Bank Fraud: Using Technology to Combat Losses arms fraud prevention professionals with authoritative guidance to detect and prevent such crimes in future.

Table of Contents



About the Author

Chapter One Bank Fraud: Then and Now

The Evolution of Fraud

The Evolution of Fraud Analysis


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


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


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


Chapter Five It Is Not Real Progress Until It Is Operational

The Importance of Presenting a Solid Picture

Building an Effective Model


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


Chapter Seven Fraud Analytics: We Are Just Scratching the Surface

A Note about the Data


Regression 1

Logistic Regression 1

“Models Should Be As Simple As Possible, But Not More So”


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


Chapter Nine The End: It Is Really the Beginning!



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