Fraud Analytics – Strategies and Methods for Detection and Prevention

<p>Foreword xi</p>
<p>Preface xiii</p>
<p>Acknowledgments xv</p>
<p>Chapter 1: The Schematics of Fraud and Fraud Analytics 1</p>
<p>How Do We Define Fraud Analytics? 2</p>
<p>Mining the Field: Fraud Analytics in its New Phase 6</p>
<p>How Do We Use Fraud Analytics? 10</p>
<p>Fraud Detection 10</p>
<p>How Do We Define Fraud Analytics? 12</p>
<p>Fraud Analytics Refined 12</p>
<p>Notes 13</p>
<p>Chapter 2: The Evolution of Fraud Analytics 15</p>
<p>Why Use Fraud Analytics? 17</p>
<p>The Evolution Continues 19</p>
<p>Fraud Prevention and Detection in Fraud Analytics 19</p>
<p>Incentives, Pressures, and Opportunities 21</p>
<p>Notes 22</p>
<p>Chapter 3: The Analytical Process and the Fraud Analytical Approach 23</p>
<p>The Turn of The Analytical Wheel 23</p>
<p>It Takes More Than One Step 24</p>
<p>Probabilities of Fraud and Where it All Begins 28</p>
<p>What Should the Fraud Analytics Process Look Like? 29</p>
<p>Data Analytics Exposed 31</p>
<p>Notes 32</p>
<p>Chapter 4: Using ACL Analytics in the Face of Excel 33</p>
<p>The Devil Remains in the Details 50</p>
<p>Notes 55</p>
<p>Chapter 5: Fraud Analytics versus Predictive Analytics 57</p>
<p>Overview of Fraud Analysis and Predictive Analysis 58</p>
<p>Comparing and Contrasting Methodologies 60</p>
<p>13 Step Score Development versus Fraud Analysis 64</p>
<p>CRISP–DM versus Fraud Data Analysis 66</p>
<p>SAS/SEMMA versus Fraud Data Analysis 68</p>
<p>Conflicts within Methodologies 69</p>
<p>Composite Methodology 70</p>
<p>Comparing and Contrasting Predictive Modeling and Data Analysis 72</p>
<p>Notes 76</p>
<p>Chapter 6: CaseWare IDEA Data Analysis Software 77</p>
<p>Detecting Fraud with IDEA 79</p>
<p>Fraud Analysis Points of IDEA 82</p>
<p>Correlation, Trend Analysis, and Time Series Analysis 83</p>
<p>What is IDEA s Purpose? 83</p>
<p>A Simple Scheme: The Purchase Fraud of an Employee as a Vendor 86</p>
<p>Stages of Using IDEA 87</p>
<p>Notes 89</p>
<p>Chapter 7: Centrifuge Analytics: Is Big Data Enough? 91</p>
<p>Sophisticated Link Analysis 92</p>
<p>The Challenge with Anti–Counterfeiting 93</p>
<p>Interactive Analytics: The Centrifuge Way 93</p>
<p>Fraud Analysis with Centrifuge VNA 95</p>
<p>The Fraud Management Process 100</p>
<p>Notes 105</p>
<p>Chapter 8: i2 Analyst′s Notebook: The Best in Fraud Solutions 107</p>
<p>Rapid Investigation of Fraud and Fraudsters 108</p>
<p>i2 Analyst s Notebook 109</p>
<p>i2 Analyst s Notebook and Fraud Analytics 113</p>
<p>How to Use i2 Analyst s Notebook: Fraud Financial Analytics 116</p>
<p>Using i2 Analyst s Notebook in a Money–Laundering Scenario 121</p>
<p>Notes 125</p>
<p>Chapter 9: The Power to Know Big Data: SAS Visual Analytics and Actionable Intelligence Technologies Financial Investigative Software 127</p>
<p>The SAS Way 127</p>
<p>Actionable Intelligence Technologies Financial Investigative Software 130</p>
<p>A Case in Point 132</p>
<p>Notes 135</p>
<p>Chapter 10: New Trends in Fraud Analytics and Tools 137</p>
<p>The Many Faces of Fraud Analytics 137</p>
<p>The Paper Chase is Over 138</p>
<p>To Be or Not to Be 140</p>
<p>Raytheon s VisuaLinks 143</p>
<p>FICO Insurance Fraud Manager 3.3 145</p>
<p>IBM i2 iBASE 146</p>
<p>Palantir Tech 147</p>
<p>Fiserv s AML Manager 148</p>
<p>Notes 148</p>
<p>About the Author 151</p>
<p>Index 153</p>
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