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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
DELENA D. SPANN, MSc, CFE, is employed with the United States Secret Service, Chicago Field Office, where she is assigned to the Electronic and Financial Crimes Task Force. Spann routinely serves on high-profile financial crimes investigations that include detecting red flags, trends, and anomalies in complex financial transactions. She is frequently called upon as a guest speaker on her expertise in fraud analytics and financial crimes. She is dedicated to the study of white-collar crime. Spann holds a bachelor's degree in liberal studies from Barry University and a master of science degree in criminal justice administration from Florida International University. She is Board of Regent (Emeritus), an Advisory Board Member, and Higher Education Committee Member of the Association of Certified Fraud Examiners; a Board of Director of ASIS International Economic Crime Council; Education Task Force Member of the Association of Certified Anti-Money Laundering Specialists; Advisory Board Member at Robert Morris University; Executive Director of the Association of Certified Fraud Examiners, Greater Chicago Chapter; a Threat Finance Task Force Member of the Association of Certified Financial Crimes Specialists; and Board of Director (Emeritus), Step Women's Network of Chicago. Spann also serves as an adjunct professor at the university/college level.
Inhaltsangabe
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
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
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