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| 001 | ocn881387996 | ||
| 003 | OCoLC | ||
| 005 | 20171029134422.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 140611s2014 nju o 001 0 eng | ||
| 010 | _a 2014022943 | ||
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_a9781118779675 _qelectronic bk. |
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| 020 | _z9781118779651 (hardback) | ||
| 020 | _z9781118936764 | ||
| 020 | _z1118936760 | ||
| 020 | _a1118779657 | ||
| 020 | _a9781118779651 | ||
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_a56CECB8B-27A5-44B2-B57C-5109AD322C76 _bOverDrive, Inc. _nhttp://www.overdrive.com |
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| 100 | 1 | _aGee, Sunder. | |
| 245 | 1 | 0 |
_aFraud and fraud detection : a data analytics approach / _cSunder Gee. _h[electronic resource] |
| 264 | 1 |
_aHoboken : _bWiley, _c2014. |
|
| 300 | _a1 online resource. | ||
| 336 |
_atext _2rdacontent |
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| 337 |
_acomputer _2rdamedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 0 | _aWiley corporate F&A series | |
| 500 | _aIncludes index. | ||
| 505 | 8 | _aMachine generated contents note: Preface Acknowledgments Chapter 1: Introduction Defining Fraud Anomalies versus Fraud Types of Fraud Assess the Risk of Fraud Conclusion Notes Chapter 2: Fraud Detection Recognizing Fraud Data Mining versus Data Analysis and Analytics Data Analytic Software Anomalies versus Fraud within Data Fraudulent Data Inclusions and Deletions Conclusion Notes Chapter 3: The Data Analysis Cycle Evaluating and Analysis Obtaining Data Files File Format Types Preparation for Data Analysis Arranging and Organizing Data Conclusion Notes Chapter 4: Statistics and Sampling Descriptive Statistics Inferential Statistics Measures of Center Measure of Dispersion Measure of Variability Sampling Conclusion Notes Chapter 5: Data Analytical Tests Benford's Law Number Duplication Test (NDT) Z- Score Relative Size Factor Test (RSF) Same -- Same -- Same Test (SSS) Same -- Same -- Different Test (SSD) Even Amounts Conclusion Notes Chapter 6: Advanced Data Analytical Tests Correlation Trend Analysis GEL -- 1 and GEL -2 Conclusion Note Chapter 7: Skimming and Cash Larceny Skimming Cash Larceny Case Study Conclusion Chapter 8: Billing Schemes 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 Cheque number sequences Conclusion Note Chapter 9: Cheque Tampering Schemes Electronic Payments Fraud Prevention Cheque Tampering Data Analytic Tests Conclusion Chapter 10: Payroll Fraud Data and Data Familiarization Data Analysis The Payroll Register Payroll Master and Commission Tests Conclusion Notes Chapter 11: Expense Reimbursement Schemes Data and Data Analysis Conclusion and Audit Trail Notes Chapter 12: Register Disbursement Schemes False Refunds and Adjustments False Voids Concealment Data Analytic Tests Conclusion Chapter 13: Non-Cash Misappropriations Types of Non-Cash Misappropriations Concealment of Non-Cash Misappropriations Data Analytics Conclusion Chapter 14: Corruption Bribery Tender Schemes Kickbacks, Illegal Gratuities and Extortion Conflict of Interest Data Analytic Tests Concealment Conclusion Chapter 15: Money Laundering The Money Laundering Process Other Money Transfer Systems and New Opportunities Audit Areas and Data Files Conclusion Notes Chapter 16: Zapper Fraud Point of Sales System Case Study Quantifying the Zapped Records Additional POS Data Files to Analyse Missing and Modified Bills The Mark-up Ratios Conclusions and Solutions Notes Chapter 17: Automation and IDEAScript Considerations for Automation Creating IDEAScripts Conclusion Chapter 18: Conclusion Financial Statement Fraud IDEA Features Demonstrated Projects Overview Data Analytics: Final Words Notes About the Companion Website About the Author Index . | |
| 520 |
_a"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"-- _cProvided by publisher. |
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| 588 | _aDescription based on print version record and CIP data provided by publisher. | ||
| 650 | 0 | _aFraud. | |
| 650 | 0 | _aManagerial accounting. | |
| 650 | 0 | _aData mining. | |
| 650 | 7 |
_aBUSINESS & ECONOMICS / Accounting / General. _2bisacsh |
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| 650 | 7 |
_aData mining. _2fast _0(OCoLC)fst00887946 |
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| 650 | 7 |
_aFraud. _2fast _0(OCoLC)fst00933786 |
|
| 650 | 7 |
_aManagerial accounting. _2fast _0(OCoLC)fst01007320 |
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| 655 | 4 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _aGee, Sunder. _tFraud and fraud detection _dHoboken : Wiley, 2014 _z9781118779651 _w(DLC) 2014021352 |
| 856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118936764 _zWiley Online Library |
| 942 |
_2ddc _cBK |
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| 999 |
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