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Fraud analytics : strategies and methods for detection and prevention /
"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 valuab...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley & Sons,
©2014.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Spann, Delena D., |d 1967- |1 https://id.oclc.org/worldcat/entity/E39PCjHmYQhxBtkPJhCMv7Brjd | |
| 245 | 1 | 0 | |a Fraud analytics : |b strategies and methods for detection and prevention / |c Delena D. Spann. |
| 260 | |a Hoboken, N.J. : |b John Wiley & Sons, |c ©2014. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 500 | |a Title from title screen. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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 as Advisory 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"-- |c Provided by publisher | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Fraud. | |
| 650 | 0 | |a Fraud investigation. | |
| 650 | 0 | |a Fraud |x Prevention. | |
| 650 | 2 | |a Fraud | |
| 650 | 6 | |a Fraude. | |
| 650 | 6 | |a Fraude |x Enquêtes. | |
| 650 | 7 | |a fraud. |2 aat | |
| 650 | 7 | |a Fraud |2 fast | |
| 650 | 7 | |a Fraud investigation |2 fast | |
| 650 | 7 | |a Fraud |x Prevention |2 fast | |
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