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"Proven guidance for expertly using analytics in fraud examinations, financial analysis,auditing and fraud preventionFraud Analytics thoroughly reveals the elements of analysis that are used in today'sfraudexaminations, fraud investigations, and financial crime investigations. This valuable resourcereviews the types of analysis that should be considered prior to beginning an investigation andexplains how to optimally use data mining techniques to detect fraud. Packed with examples andsample cases illustrating pertinent concepts in practice, this book also explores the two majordata 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 CertifiedFraud Examiners (ACFE), who currently serves asAdvisory Board Member of the Association ofCertified Fraud Examiners, Board Member of the Education Task Force of the Association ofCertified Anti-Money Laundering Specialists ASIS International (Economic Crime Council) andAdvisory Board Member of the Robert Morris University (School of Business), FraudAnalyticsequips you with authoritative fraud analysis techniques you can put to use right away"--
List of contents
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
