Cover image for Forensic analytics : methods and techniques for forensic accounting investigations
Forensic analytics : methods and techniques for forensic accounting investigations
Nigrini, Mark J. (Mark John)
Publication Information:
Hoboken, N.J. : Wiley, [2011]

Physical Description:
xvi, 458 pages : illustrations ; 26 cm.
"The book will review and discuss (with Access and Excel examples) the methods and techniques that investigators can use to uncover anomalies in corporate and public sector data. These anomalies would include errors, biases, duplicates, number rounding, and omissions. The focus will be the detection of fraud, intentional errors, and unintentional errors using data analytics. Despite the quantitative and computing bias, the book will still be interesting to read with interesting vignettes and illustrations. Most chapters will be understandable by accountants and auditors that usually are lacking in the rigors of mathematics and statistics. The data interrogation methods are based on (a) known statistical techniques, and (b) the author's own published research in the field"--
Format :


Call Number
Material Type
Home Location
Item Holds
HV6768 .N54 2011 Adult Non-Fiction Non-Fiction Area

On Order



Discover how to detect fraud, biases, or errors in your datausing Access or Excel

With over 300 images, Forensic Analytics reviews andshows how twenty substantive and rigorous tests can be used todetect fraud, errors, estimates, or biases in your data. For eachtest, the original data is shown with the steps needed to get tothe final result. The tests range from high-level data overviews toassess the reasonableness of data, to highly focused tests thatgive small samples of highly suspicious transactions. These testsare relevant to your organization, whether small or large, forprofit, nonprofit, or government-related.

Demonstrates how to use Access, Excel, and PowerPoint in aforensic setting Explores use of statistical techniques such as Benford's Law,descriptive statistics, correlation, and time-series analysis todetect fraud and errors Discusses the detection of financial statement fraud usingvarious statistical approaches Explains how to score locations, agents, customers, oremployees for fraud risk Shows you how to become the data analytics expert in yourorganization

Forensic Analytics shows how you can use Microsoft Accessand Excel as your primary data interrogation tools to findexceptional, irregular, and anomalous records.

Author Notes

Mark J. Nigrini, PhD , is an Associate Professor at The College of New Jersey, where he teaches auditing and forensic accounting. His current research addresses forensic and continuous monitoring techniques and advanced theoretical work on Benford's Law. Dr. Nigrini has published his Benford's Law and forensic accounting research in academic journals and in professional accounting and auditing publications. He has been interviewed on radio and television and his work has been discussed in publications including the Wall Street Journal and the New York Times.

Table of Contents

Preface xi
About the Author xv
Chapter 1 Using Access in Forensic Investigationsp. 1
An Introduction to Accessp. 2
The Architecture of Accessp. 4
A Review of Access Tablesp. 6
Importing Data into Accessp. 8
A Review of Access Queriesp. 10
Converting Excel Data into a Usable Access Formatp. 13
Using the Access Documenterp. 20
Database Limit of 2 GBp. 24
Miscellaneous Access Notesp. 24
Summaryp. 25
Chapter 2 Using Excel in Forensic Investigationsp. 27
Pitfalls in Using Excelp. 28
Importing Data into Excelp. 30
Reporting Forensic Analytics Resultsp. 32
Protecting Excel Spreadsheetsp. 34
Using Excel Results in Word Filesp. 36
Excel Warnings and Indicatorsp. 40
Summaryp. 41
Chapter 3 Using PowerPoint in Forensic Presentationsp. 43
Overview of Forensic Presentationsp. 44
An Overview of PowerPointp. 44
Planning the Presentationp. 45
Color Schemes for Forensic Presentationsp. 46
Problems with Forensic Reportsp. 50
Summaryp. 61
Chapter 4 High-Level Data Overview Testsp. 63
The Data Profilep. 64
The Data Histogramp. 67
The Periodic Graphp. 69
Preparing the Data Profile Using Accessp. 70
Preparing the Data Profile Using Excelp. 77
Calculating the Inputs for the Periodic Graph in Accessp. 79
Preparing a Histogram in Access Using an Interval Tablep. 81
Summaryp. 83
Chapter 5 Benford's Law: The Basicsp. 85
An Overview of Benford's Lawp. 86
From Theory to Application inp. 60
Yearsp. 89
Which Data Sets Should Conform to Benford's Law?p. 97
The Effect of Data Set Sizep. 98
The Basic Digit Testsp. 99
Running the First-Two Digits Test in Accessp. 102
Summaryp. 107
Chapter 6 Benford's Law: Assessing Conformityp. 109
One Digit at a Time: The Z-Statisticp. 110
The Chi-Square and Kolmogorov-Smirnoff Testsp. 111
The Mean Absolute Deviation (MAD) Testp. 114
Tests Based on the Logarithmic Basis of Benford's Lawp. 115
Creating a Perfect Synthetic Benford Setp. 121
The Mantissa Arc Testp. 122
Summaryp. 129
Chapter 7 Benford's Law: The Second-Order and Summation Testsp. 130
A Description of the Second-Order Testp. 131
The Summation Testp. 144
Summaryp. 151
Chapter 8 Benford's Law: The Number Duplication and Last-Two Digits Testsp. 153
The Number Duplication Testp. 154
Running the Number Duplication Test in Accessp. 155
Running the Number Duplication Test in Excelp. 164
The Last-Two Digits Testp. 167
Summaryp. 172
Chapter 9 Testing the Internal Diagnostics of Current Period and Prior Period Datap. 173
A Review of Descriptive Statisticsp. 175
An Analysis of Alumni Giftsp. 178
An Analysis of Fraudulent Datap. 182
Summary and Discussionp. 189
Chapter 10 Identifying Fraud Using the Largest Subsets and Largest Growth Testsp. 191
Findings From the Largest Subsets Testp. 193
Running the Largest Subsets Test in Accessp. 195
Running the Largest Growth Test in Accessp. 197
Running the Largest Subsets Test in Excelp. 200
Running the Largest Growth Test in Excelp. 203
Summaryp. 210
Chapter 11 Identifying Anomalies Using the Relative Size Factor Testp. 212
Relative Size Factor Test Findingsp. 213
Running the RSF Testp. 215
Running the Relative Size Factor Test in Accessp. 216
Running the Relative Size Factor Test in Excelp. 226
Summaryp. 232
Chapter 12 Identifying Fraud Using Abnormal Duplications within Subsetsp. 233
The Same-Same-Same Testp. 234
The Same-Same-Different Testp. 235
The Subset Number Duplication Testp. 236
Running the Same-Same-Same Test in Accessp. 238
Running the Same-Same-Different Test in Accessp. 239
Running the Subset Number Duplication Test in Accessp. 244
Running the Same-Same-Same Test in Excelp. 248
Running the Same-Same-Different Test in Excelp. 252
Running the Subset Number Duplication Test in Excelp. 256
Summaryp. 262
Chapter 13 Identifying Fraud Using Correlationp. 263
The Concept of Correlationp. 264
Correlation Calculationsp. 272
Using Correlation to Detect Fraudulent Sales Numbersp. 272
Using Correlation to Detect Electricity Theftp. 276
Using Correlation to Detect Irregularities in Election Resultsp. 278
Detecting Irregularities in Pollution Statisticsp. 282
Calculating Correlations in Accessp. 287
Calculating the Correlations in Excelp. 291
Summaryp. 295
Chapter 14 Identifying Fraud Using Time-Series Analysisp. 297
Time-Series Methodsp. 299
An Application Using Heating Oil Salesp. 299
An Application Using Stock Market Datap. 303
An Application Using Construction Datap. 306
An Analysis of Streamflow Datap. 313
Running Time-Series Analysis in Excelp. 319
Calculating the Seasonal Factorsp. 320
Running a Linear Regressionp. 322
Fitting a Curve to the Historical Datap. 324
Calculating the Forecastsp. 325
Summaryp. 330
Chapter 15 Fraud Risk Assessments of Forensic Unitsp. 332
The Risk Scoring Methodp. 333
The Forensic Analytics Environmentp. 335
A Description of the Risk-Scoring Systemp. 336
P1: High Food and Supplies Costsp. 338
P2: Very High Food and Supplies Costsp. 339
P3: Declining Salesp. 340
P4: Increase in Food Costsp. 342
P5: Irregular Seasonal Pattern for Salesp. 344
P6: Round Numbers Reported as Sales Numbersp. 346
P7: Repeating Numbers Reported as Sales Numbersp. 347
P8: Inspection Rankingsp. 347
P9: High Receivable Balancep. 348
P10: Use of Automated Reporting Proceduresp. 348
Final Resultsp. 349
An Overview of the Reporting System and Future Plansp. 350
Some Findingsp. 351
Discussionp. 353
Summaryp. 353
Chapter 16 Examples of Risk Scoring with Access Queriesp. 355
The Audit Selection Method of the IRSp. 356
Risk Scoring to Detect Banking Fraudp. 360
Final Risk Scoresp. 364
Risk Scoring to Detect Travel Agent Fraudp. 364
Final Resultsp. 369
Risk Scoring to Detect Vendor Fraudp. 369
Vendor Risk Scoring Using Accessp. 376
Summaryp. 385
Chapter 17 The Detection of Financial Statement Fraudp. 388
The Digits of Financial Statement Numbersp. 388
Detecting Biases in Accounting Numbersp. 395
An Analysis of Enron's Reported Numbersp. 398
An Analysis of Biased Reimbursement Numbersp. 399
Detecting Manipulations in Monthly Subsidiary Reportsp. 404
Predictor Weightingsp. 421
Conclusionsp. 423
Summaryp. 424
Chapter 18 Using Analytics on Purchasing Card Transactionsp. 425
Purchasing Cardsp. 426
The National Association of Purchasing Card Professionalsp. 432
A Forensic Analytics Dashboardp. 433
An Example of Purchasing Card Datap. 433
High-Level Data Overviewp. 435
The First-Order Testp. 438
The Summation Testp. 440
The Last-Two Digits Testp. 440
The Second-Order Testp. 441
The Number Duplication Testp. 442
The Largest Subsets Testp. 444
The Same-Same-Same Testp. 446
The Same-Same-Different Testp. 446
The Relative Size Factor Testp. 448
Conclusions with Respect to Card Purchasesp. 449
A Note on Microsoft Officep. 450
A Note on the Forensic Analytic Testsp. 451
Conclusionp. 452
Referencesp. 455
Indexp. 459