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Fraud Analytics: Strategies and Methods for Detection and Prevention

Fraud Analytics: Strategies and Methods for Detection and Prevention

Delena D. Spann

ISBN: 978-1-118-23068-8

Oct 2013

176 pages

In Stock

$50.00

Description

Proven guidance for expertly using analytics in fraud examinations, financial analysis, auditing and fraud prevention

Fraud Analytics thoroughly reveals the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. This valuable resource reviews the types of analysis that should be considered prior to beginning an investigation and explains how to optimally use data mining techniques to detect fraud. Packed with examples and sample cases illustrating pertinent concepts in practice, this book also explores the two major data analytics providers: ACL and IDEA.

  • Looks at elements of analysis used in today's fraud examinations
  • Reveals how to use data mining (fraud analytic) techniques to detect fraud
  • Examines ACL and IDEA as indispensable tools for fraud detection
  • Includes an abundance of sample cases and examples

Written by Delena D Spann, Board of Regent (Emeritus) for the Association of Certified Fraud Examiners (ACFE), who currently serves as Advisory Board Member of the Association of Certified Fraud Examiners, Board Member of the Education Task Force of the Association of Certified Anti-Money Laundering Specialists ASIS International (Economic Crime Council) and Advisory Board Member of the Robert Morris University (School of Business), Fraud Analytics equips you with authoritative fraud analysis techniques you can put to use right away.

Foreword xi

Preface xiii

Acknowledgments xv

Chapter 1: The Schematics of Fraud and Fraud Analytics 1

How Do We Define Fraud Analytics? 2

Mining the Field: Fraud Analytics in its New Phase 6

How Do We Use Fraud Analytics? 10

Fraud Detection 10

How Do We Define Fraud Analytics? 12

Fraud Analytics Refined 12

Notes 13

Chapter 2: The Evolution of Fraud Analytics 15

Why Use Fraud Analytics? 17

The Evolution Continues 19

Fraud Prevention and Detection in Fraud Analytics 19

Incentives, Pressures, and Opportunities 21

Notes 22

Chapter 3: The Analytical Process and the Fraud Analytical Approach 23

The Turn of The Analytical Wheel 23

It Takes More Than One Step 24

Probabilities of Fraud and Where it All Begins 28

What Should the Fraud Analytics Process Look Like? 29

Data Analytics Exposed 31

Notes 32

Chapter 4: Using ACL Analytics in the Face of Excel 33

The Devil Remains in the Details 50

Notes 55

Chapter 5: Fraud Analytics versus Predictive Analytics 57

Overview of Fraud Analysis and Predictive Analysis 58

Comparing and Contrasting Methodologies 60

13 Step Score Development versus Fraud Analysis 64

CRISP-DM versus Fraud Data Analysis 66

SAS/SEMMA versus Fraud Data Analysis 68

Conflicts within Methodologies 69

Composite Methodology 70

Comparing and Contrasting Predictive Modeling and Data Analysis 72

Notes 76

Chapter 6: CaseWare IDEA Data Analysis Software 77

Detecting Fraud with IDEA 79

Fraud Analysis Points of IDEA 82

Correlation, Trend Analysis, and Time Series Analysis 83

What is IDEA’s Purpose? 83

A Simple Scheme: The Purchase Fraud of an Employee as a Vendor 86

Stages of Using IDEA 87

Notes 89

Chapter 7: Centrifuge Analytics: Is Big Data Enough? 91

Sophisticated Link Analysis 92

The Challenge with Anti-Counterfeiting 93

Interactive Analytics: The Centrifuge Way 93

Fraud Analysis with Centrifuge VNA 95

The Fraud Management Process 100

Notes 105

Chapter 8: i2 Analyst's Notebook: The Best in Fraud Solutions 107

Rapid Investigation of Fraud and Fraudsters 108

i2 Analyst’s Notebook 109

i2 Analyst’s Notebook and Fraud Analytics 113

How to Use i2 Analyst’s Notebook: Fraud Financial Analytics 116

Using i2 Analyst’s Notebook in a Money-Laundering Scenario 121

Notes 125

Chapter 9: The Power to Know Big Data: SAS Visual Analytics and Actionable Intelligence Technologies’ Financial Investigative Software 127

The SAS Way 127

Actionable Intelligence Technologies’ Financial Investigative Software 130

A Case in Point 132

Notes 135

Chapter 10: New Trends in Fraud Analytics and Tools 137

The Many Faces of Fraud Analytics 137

The Paper Chase is Over 138

To Be or Not to Be 140

Raytheon’s VisuaLinks 143

FICO Insurance Fraud Manager 3.3 145

IBM i2 iBASE 146

Palantir Tech 147

Fiserv’s AML Manager 148

Notes 148

About the Author 151

Index 153