Fraud Analytics : Strategies and Methods for Detection and Prevention
By: Spann, Delena D.Material type: TextSeries: eBooks on Demand.Wiley Corporate F&A: Publisher: Hoboken : Wiley, 2014Description: 1 online resource (174 p.).ISBN: 9781118286999.Subject(s): Fraud -- Prevention | Fraud investigation | FraudGenre/Form: Electronic books.Additional physical formats: Print version:: Fraud Analytics : Strategies and Methods for Detection and PreventionDDC classification: 658.473 Online resources: Click here to view this ebook.
|Item type||Current location||Call number||URL||Status||Date due||Barcode|
|Electronic Book||UT Tyler Online Online||HV8079.F7 .S68 2014 (Browse shelf)||http://uttyler.eblib.com/patron/FullRecord.aspx?p=1752695||Available||EBL1752695|
Fraud Analytics: Strategies and Methods for Detection and Prevention; Contents; Foreword; Preface; Acknowledgments; Chapter 1: The Schematics of Fraud and Fraud Analytics; How Do We Define Fraud Analytics?; Mining the Field: Fraud Analytics in its New Phase; How Do We Use Fraud Analytics?; Fraud Detection; How Do We Define Fraud Analytics?; Fraud Analytics Refined; Notes; Chapter 2: The Evolution of Fraud Analytics; Why Use Fraud Analytics?; The Evolution Continues; Fraud Prevention and Detection in Fraud Analytics; Incentives, Pressures, and Opportunities; Notes
Chapter 3: The Analytical Process and the Fraud Analytical ApproachThe Turn of The Analytical Wheel; It Takes More Than One Step; Probabilities of Fraud and Where it All Begins; What Should the Fraud Analytics Process Look Like?; Data Analytics Exposed; Notes; Chapter 4: Using ACL Analytics in the Face of Excel; The Devil Remains in the Details; Notes; Chapter 5: Fraud Analytics versus Predictive Analytics; Overview of Fraud Analysis and Predictive Analysis; Comparing and Contrasting Methodologies; 13 Step Score Development versus Fraud Analysis; CRISP-DM versus Fraud Data Analysis
SAS/SEMMA versus Fraud Data AnalysisConflicts within Methodologies; Composite Methodology; Comparing and Contrasting Predictive Modeling and Data Analysis; Notes; Chapter 6: CaseWare IDEA Data Analysis Software; Detecting Fraud with IDEA; Fraud Analysis Points of IDEA; Correlation, Trend Analysis, and Time Series Analysis; What is IDEA's Purpose?; A Simple Scheme: The Purchase Fraud of an Employee as a Vendor; Stages of Using IDEA; Notes; Chapter 7: Centrifuge Analytics: Is Big Data Enough?; Sophisticated Link Analysis; The Challenge with Anti-Counterfeiting
Interactive Analytics: The Centrifuge WayFraud Analysis with Centrifuge VNA; The Fraud Management Process; Notes; Chapter 8: i2 Analyst''s Notebook: The Best in Fraud Solutions; Rapid Investigation of Fraud and Fraudsters; i2 Analyst's Notebook; i2 Analyst's Notebook and Fraud Analytics; How to Use i2 Analyst's Notebook: Fraud Financial Analytics; Using i2 Analyst's Notebook in a Money-Laundering Scenario; Notes; Chapter 9: The Power to Know Big Data: SAS Visual Analytics and Actionable Intelligence Technologies' Financial Investigative Software; The SAS Way
Actionable Intelligence Technologies' Financial Investigative SoftwareA Case in Point; Notes; Chapter 10: New Trends in Fraud Analytics and Tools; The Many Faces of Fraud Analytics; The Paper Chase is Over; To Be or Not to Be; Raytheon's VisuaLinks; FICO Insurance Fraud Manager 3.3; IBM i2 iBASE; Palantir Tech; Fiserv's AML Manager; Notes; About the Author; Index
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''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
Description based upon print version of record.