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Summary:

"Detect fraud faster--no matter how well hidden--with IDEA automationFraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book.Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification"-- [Provided by publisher]

Table of Contents:

• Machine generated contents note: Defining Fraud
• Anomalies versus Fraud
• Types of Fraud
• Assess the Risk of Fraud
• Conclusion
• Notes
• Recognizing Fraud
• Data Mining versus Data Analysis and Analytics
• Data Analytical Software
• Anomalies versus Fraud within Data
• Fraudulent Data Inclusions and Deletions
• Conclusion
• Notes
• Evaluation and Analysis
• Obtaining Data Files
• Performing the Audit
• File Format Types
• Preparation for Data Analysis
• Arranging and Organizing Data
• Conclusion

• Notes
• Descriptive Statistics
• Inferential Statistics
• Measures of Center
• Measure of Dispersion
• Measure of Variability
• Sampling
• Conclusion
• Notes
• Benford's Law
• Number Duplication Test
• Z-Score
• Relative Size Factor Test
• Same-Same-Same Test
• Same-Same-Different Test
• Even Amounts
• Conclusion
• Notes
• Correlation
• Trend Analysis
• GEL-1 and GEL-2
• Conclusion
• Note
• Skimming
• Cash Larceny
• Case Study
• Conclusion
• Data and Data Familiarization
• Benford's Law Tests
• Relative Size Factor Test
• Z-Score
• Even Dollar Amounts
• Same-Same-Same Test
• Same-Same-Different Test
• Payments without Purchase Orders Test
• Length of Time between Invoice and Payment Dates Test
• Search for Post Office Box
• Match Employee Address to Supplier
• Duplicate Addresses in Vendor Master
• Payments to Vendors Not in Master
• Gap Detection of Check Number Sequences
• Conclusion
• Note
• Electronic Payments Fraud Prevention
• Check Tampering
• Data Analytical Tests
• Conclusion
• Data and Data Familiarization
• Data Analysis
• Payroll Register
• Payroll Master and Commission Tests
• Conclusion
• Notes
• Data and Data Analysis
• Conclusion and Audit Trail
• Notes
• False Refunds and Adjustments
• False Voids
• Concealment
• Data Analytical Tests
• Conclusion
• Types of Noncash Misappropriations
• Concealment of Noncash Misappropriations
• Data Analytics
• Conclusion
• Bribery
• Tender Schemes
• Kickbacks, Illegal Gratuities, and Extortion
• Conflict of Interest
• Data Analytical Tests
• Concealment
• Conclusion
• Money-Laundering Process
• Other Money Transfer Systems and New Opportunities
• Audit Areas and Data Files
• Conclusion
• Point-of-Sales System Case Study
• Quantifying the Zapped Records
• Additional POS Data Files to Analyze
• Missing and Modified Bills
• Markup Ratios
• Conclusions and Solutions
• Notes
• Considerations for Automation
• Creating IDEAScripts
• Conclusion
• Financial Statement Fraud
• IDEA Features Demonstrated
• Projects Overview
• Data Analytics: Final Words
• Notes.

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Author/Creator:

Gee, Sunder

Languages:

English

Language Notes:

Item content: English

Main Work:

Fraud and fraud detection: a data analytics approach [by Gee, S.]

Other Related Resources:

Print version: Fraud and fraud detection [by Gee, S.] (Hoboken : Wiley, 2014 — ISBN 9781118779651; LCCN 2014021352)

Related Series:

Wiley corporate F&A series

Subjects:

Fraud

Managerial accounting

Data mining

General Notes:

Includes index.
Electronic reproduction. Ipswich, MA Available via World Wide Web.
Description based on print version record and CIP data provided by publisher.

Physical Description:

1 online resource.

Call Numbers:

HV6691 .G44 2014

ISBNs:

9781118779668 (epub)
1118779665 (epub)
9781118779675 (pdf)
1118779673 (pdf)
9781118779651 (hardback) [Invalid]
9781118936764
1118936760

Library of Congress Control Numbers:

2014022943

OCLC Numbers:

881387996

Other Control Numbers:

887094 (source: EbpS)
[Unknown Type]: ybp12157054