Fraud and Fraud Detection :A Data Analytics Approach ( Wiley Corporate F&A )

Publication subTitle :A Data Analytics Approach

Publication series :Wiley Corporate F&A

Author: Sunder Gee  

Publisher: John Wiley & Sons Inc‎

Publication year: 2014

E-ISBN: 9781118779675

P-ISBN(Hardback):  9781118779651

Subject: D917.6 Crimes Prevention and Governance

Keyword: nullnull

Language: ENG

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Description

Detect fraud faster—no matter how well hidden—with IDEA automation

Fraud 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.

Chapter

Fraudulent Data Inclusions and Deletions

Conclusion

Notes

Chapter 3: The Data Analysis Cycle

Evaluation and Analysis

Software and Technology

Audit and Investigative

Obtaining Data Files

Audit Objectives

Determine Whether IDEA Is Appropriate

Data Requirements

Performing the Audit

Obtain Test Files

IDEA Import

After Import

Cleaning Up the Data

Documenting the Results

File Format Types

Preparation for Data Analysis

Data Familiarization

Arranging and Organizing Data

Conclusion

Notes

Chapter 4: Statistics and Sampling

Descriptive Statistics

Inferential Statistics

Measures of Center

Measure of Dispersion

Measure of Variability

Deviations from the Mean

The Mean Deviation

The Variance

The Standard Deviation

Sampling

Statistical Sampling

Nonstatistical Sampling

Sampling Risk

Nonsampling Risk

Nonstatistical Sampling Methods

Statistical Sampling Methods

Conclusion

Notes

Chapter 5: Data Analytical Tests

Benford’s Law

Using Benford’s Law in IDEA

Number Duplication Test

Z-Score

Relative Size Factor Test

Same-Same-Same Test

Same-Same-Different Test

Even Amounts

Conclusion

Notes

Chapter 6: Advanced Data Analytical Tests

Correlation

Trend Analysis

GEL-1 and GEL-2

GEL‐1

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 Check Number Sequences

Conclusion

Note

Chapter 9: Check-Tampering Schemes

Electronic Payments Fraud Prevention

Check Tampering

Obtaining Checks

Obtaining an Authorized Signature

The Payee

Concealing the Deed

Data Analytical Tests

Other Analytical Tests

Conclusion

Chapter 10: Payroll Fraud

Data and Data Familiarization

Data Familiarization Steps

Data Analysis

Blank Contents in the Last-Name Field

High Number of Payments

Duplicate Key Detection

Payments on Last Day of the Month

Analyzing Files by Payment Type

Regular Salary Payments

Overtime Wages Payments

Signing Incentive Plan Payments

Comparing Regular Salary Payments with Other Payment Types

The Payroll Register

Payroll Register Tests

Payroll Master and Commission Tests

Conclusion

Notes

Chapter 11: Expense Reimbursement Schemes

Data and Data Analysis

Days Traveled

Z‐Score Tests

Pivot Table View

Top Record Extractions

Same‐Same‐Same Tests

Same‐Same‐Different Test

Even Amounts Tests

Daily Averages Tests

Relative Size Factor Test

Comparison Based on Audit Unit

Travel Outside of the Country Test

Other Potential Tests

Purchase Cards

Conclusion and Audit Trail

Notes

Chapter 12: Register Disbursement Schemes

False Refunds and Adjustments

False Voids

Concealment

Data Analytical Tests

Data Familiarization

Void Tests

Coupon Tests

Other Data Analytical Tests

Conclusion

Chapter 13: Noncash Misappropriations

Types of Noncash Misappropriations

Misuse and Abuse

Unconcealed Misappropriations

Transfer of Assets

Proprietary Information

Concealment of Noncash Misappropriations

Falsifying Sales or Purchase Records

Falsifying Inventory Records

Data Analytics

Data Files

Data Analytical Tests

Conclusion

Chapter 14: Corruption

Bribery

Overbilling Schemes

Underbilling Schemes

Tender Schemes

Kickbacks, Illegal Gratuities, and Extortion

Conflict of Interest

Data Analytical Tests

Other Data Analytical Tests

Concealment

Conclusion

Chapter 15: Money Laundering

The Money-Laundering Process

The Placement Stage

The Layering Stage

The Integration Stage

Other Money Transfer Systems and New Opportunities

Audit Areas and Data Files

Data Analytical Tests

Conclusion

Chapter 16: Zapper Fraud

Point-of-Sales System Case Study

File Preparation

Gap Detection

Analysis of POS Data

Quantifying the Zapped Records

Additional POS Data Files to Analyze

Missing and Modified Bills

The Markup Ratios

Conclusions and Solutions

Notes

Chapter 17: Automation and IDEAScript

Considerations for Automation

Visual Script versus IDEAScript

Creating IDEAScripts

Companion Website IDEAScripts

Conclusion

Chapter 18: Conclusion

Financial Statement Fraud

IDEA Features Demonstrated

Projects Overview

Data Analytics: Final Words

Notes

About the Author

About the Website

Index

EULA

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