Overview

Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomalies Shows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the tests Applies the tests under review in each chapter to the same purchasing card data from a government entity Includes interesting cases studies throughout that are linked to the tests being reviewed. Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levels Includes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chapter questions, and end of chapter cases. Written by a prominent educator and researcher in forensic accounting and auditing, the new edition of Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations is an essential resource for forensic accountants, auditors, comptrollers, fraud investigators, and graduate students.

Full Product Details

Author:   Mark J. Nigrini
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Edition:   2nd edition
Dimensions:   Width: 19.10cm , Height: 3.60cm , Length: 25.70cm
Weight:   1.066kg
ISBN:  

9781119585763

ISBN 10:   1119585767
Pages:   544
Publication Date:   06 July 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

"List of Cases xiii About the Author xv Preface xvii Abbreviations xxi Analytics Software Used xxv Introduction 1 Temptation in an Occupation 2 Fraudulent Checks Written by the CFO 4 Fraudulent Purchases Made by a Purchasing Manager 7 Donna was a Gamblin’ Wreck at Georgia Tech 9 Forensic Analytics 11 An Overview of Tableau 13 The Risk Assessment Standards 19 Discussion 21 Chapter 1: Using Microsoft Excel for Forensic Analytics 23 The Fraud Types Relevant to Forensic Analytics 23 The Main Steps in a Forensic Analytics Application 25 The Final Report 27 An Overview of Excel 28 Importing Data into Excel 29 Some Useful Excel Formatting Features 30 Protecting Excel Spreadsheets 32 The Valuable ""IF"" Function 33 The PIVOTTABLE Routine 36 The Valuable VLOOKUP Function 38 Using Excel Results in Word Files 40 Excel Warnings and Indicators 42 Excel Dashboards 43 Dashboards in Practice 46 Summary 47 Chapter 2: The Initial High-Level Overview Tests 50 The Data Profile 51 The Histogram 56 The Periodic Graph 58 Descriptive Statistics 60 Preparing the Data Profile Using Excel 62 Preparing the Data Profile Using Access 64 Preparing the Histogram in Excel and Access 68 Preparing the Histogram in IDEA and Tableau 72 Preparing the Periodic Graph in Excel and Access 74 Summary 76 Chapter 3: Benford’s Law: The Basic Tests 79 An Overview of Benford’s Law 80 Some Early Discussions of Benford’s Law 83 Selected Articles from the Eighties 85 Selected Articles from the Nineties 88 Scenarios Under Which Data Should Conform to Benford 90 The Two Scenarios Under Which Accounting Data Sets Should Conform to Benford 93 Other Considerations for the Conformity of Accounting Data 94 Accounting Data Examples 95 Preparing the Benford Graph Using Excel 98 Preparing the Benford Graph Using Access 99 Summary 101 Chapter 4: Benford’s Law: Advanced Topics 103 Conformity and the Likelihood of Material Errors 103 The First Digits Versus the First-Two Digits 107 Measuring Conformity Using the Z-Statistic 109 The Chi-Square and the Kolmogorov-Smirnoff Tests of Conformity 111 The Mean Absolute Deviation (MAD) Test 112 The Effect of Data Set Size of Conformity to Benford 114 Using Benford’s Law in a Forensic Accounting Setting 116 Using Benford’s Law for Journal Entries in an External Audit 119 Using Benford for Subsidiary Ledger Balances in an External Audit 123 Preparing the Benford Graph in Excel 125 Summary 126 Chapter 5: Benford’s Law: Completing The Cycle 127 The Number Duplication Test 127 The Number Duplications in Accounting Textbooks 132 The Electric Utility Company Fraud Case 134 The Petty Cash Fraud Scheme 136 The Last-Two Digits Test 139 The Fraudulent Credit Card Sales Scheme 141 The Missing Cash Sales Case 142 Running the Number Duplication Test in Excel 144 Running the Number Duplication Test in Access 146 Running the Last-Two Digits Test in Excel 148 Running the Last-Two Digits Test in Access 149 Running the Number Duplication Test in R 151 Summary 153 Chapter 6: Identifying Anomalous Outliers: Part 1 154 The Summation Test 155 The Fraud That Was Red Flagged by Two Qualitative Outliers 158 The Largest Subsets Test 161 The Largest Subset Growth Test 165 The School District Transportation Fraud 168 The SkyBonus Fraud Scheme 170 Running the Summation Test in Excel 170 Running the Summation Test in Access 171 Running the Largest Subsets Test in Excel 172 Running the Largest Subsets Test in Access 173 Running the Largest Growth Test in Excel 174 Running the Largest Growth Test in Access 176 Running the Largest Subsets Test in R 179 Summary 180 Chapter 7: Identifying Anomalous Outliers: Part 2 182 Examples of Relative Size Factor Test Findings 184 The Scheme That Used a Vault That Was Over Capacity 186 The Scheme That Added Sold Cars to the Car Inventory Account 189 The Vice Chairman of the Board Who Stole 0.5 Percent of His Salary 193 Running the RSF Test in Excel 194 Running the RSF Test in Access 199 Running the RSF Test in SAS 208 Summary 212 Chapter 8: Identifying Abnormal Duplications 214 The Same-Same-Same Test 215 Duplicate Payments and Various Types of Fraud 217 The Same-Same-Different (Near-Duplicates) Test 220 The Near-Duplicates Fraud Scheme: Introduction 221 The Near-Duplicates Fraud: The Act 222 The Near-Duplicates Fraud: Getting the Legal Process Started 224 The Near-Duplicates Fraud: Two Sentencing Hearings 228 The Near-Duplicates Fraud: Epilogue 230 The Subset Number Duplication Test 231 Running the Same-Same-Same Test in Excel 233 Running the Same-Same-Different Test in Excel 235 Running the Subset Number Frequency Test in Excel 237 Running the Same-Same-Same Test in Access 239 Running the Same-Same-Different Test in Access 240 Running the Subset Number Frequency Test in Access 242 Summary 245 Chapter 9: Comparing Current Period and Prior Period Data: Part 1 247 A Review of Descriptive Statistics 249 An Analysis of the Purchasing Card Data 250 My Law: An Analysis of Payroll Data 255 An Analysis of Machine Learning Data 257 An Analysis of Grocery Store Sales 261 The Scheme That Used Bank Transfers to a Secret Bank Account 263 Running the Descriptive Statistics Tests in Excel 268 Running the Descriptive Statistics Tests in Minitab 269 Running the Descriptive Statistics Tests in SAS 270 Summary and Discussion 272 Chapter 10: Comparing Current Period and Prior Period Data: Part 2 274 Vectors and Measures of Change 275 An Analysis of the Purchasing Card Data 280 Taxpayer Identity Theft Refund Fraud 282 The Tax Return That Omitted a Million Dollar Prize 284 The Tax Returns for 2000 and 2001 285 The Indictment for Tax Evasion 291 The Tax Evasion Trial 292 The Verdict and Sentencing 298 An Analysis of Joe Biden’s Tax Returns 299 Running the VVS Test in SAS 303 Summary and Discussion 304 Chapter 11: Identifying Anomalies In Time-Series Data 306 An Analysis of the Purchasing Card Data 307 Using IDEA for Time-Series Analysis 311 The Fraud Scheme That Withdrew Funds from Customer Accounts 312 Employee Data Access After Termination 317 A Time-Series Analysis of Grocery Store Sales 321 Using Correlation to Detect Fraud and Errors 322 Using the Angle θ on Trial Balance Data 324 Using the VVS on Customer Rebates 327 Showing the VVS Results in a Dashboard 332 Running Time-Series Analysis in SAS 334 Summary and Discussion 336 Chapter 12: Scoring Forensic Units for Fraud Risk 338 An Overview of Risk Scoring 339 The Audit Selection Method of the IRS 340 The Fraudulent Vendor with a Post Office Box in the Head Office 344 Risk Scoring to Detect Vendor Fraud 348 Risk Scoring to Detect Errors in Sales Reports 354 The Predictors Used in the Sales Report Scoring Model 356 The Results of the Sales Report Scoring Model 364 Summary and Discussion 365 Chapter 13: Case Study: An Employee’s Fraudulent Tax Refunds 367 Background Information 368 The Nicest Person in the Office 369 The Early Years of Tax Refund Fraud Scheme 372 The Later Years of Tax Refund Fraud Scheme 375 An Analysis of the Fraudulent Refund Amounts 376 The End Was Nigh 383 The Letter of the Law 386 Sentencing 391 Mary Ayers-Zander 392 Epilogue 393 Appendix 13A: The Fraudulent Refunds 394 Chapter 14: Case Study: A Supplier’s Fraudulent Shipping Claims 401 Background Information 401 The Fraudulent Shipping Charges Scheme 403 An Analysis of the Shipping Charges 405 Charlene’s Lifestyle 408 The Scheme is Discovered 409 The Corley Plea 412 Charlene’s Appeal for a Reduced Sentence 415 The Government’s Response to Charlene’s Memorandum 417 The Sentencing Hearing 417 The Sentence 419 Motion to Delay the Prison Term 420 Conclusions 423 Chapter 15: Detecting Financial Statement Fraud 425 An Overview of Financial Statement Fraud 426 Biases in Financial Statement Numbers 427 Enron’s Financial Statements 430 Enron’s Chief Financial Officer 432 HealthSouth’s Financial Statements 433 WorldCom’s Financial Statements 436 WorldCom’s Rounded Numbers 440 Using Benford’s Law to Detect Financial Statement Misconduct 442 Beneish’s M-Score 446 Detection and Investigation Steps 447 Detecting Manipulations in Monthly Subsidiary Reports 449 Summary 454 Chapter 16: Using Microsoft Access and R For Analytics 455 An Introduction to Access 456 The Architecture of Access 457 A Review of Access Tables 459 Importing Data into Access 461 A Review of Access Queries 462 Converting Excel Data into a Usable Access Format 465 Using the Access Documenter 466 Database Limit of 2 GB 468 Reports 469 Miscellaneous Access Notes 471 An Introduction to R 472 Installing R and R Studio 472 The Advantages of Using R 474 R Markdown 475 Running Arithmetic Code in R 475 Calculating the VVS in R 477 Summary 479 Appendix 16A: A Discussion of the Basic Commands 480 Chapter 17: Concluding Notes on Fraud Prevention and Detection 482 The Annual Cost of Employee Fraud 483 The Legal Process 484 ”I’m a Lawyer, Trust [Account] Me” 485 The Rights of the Defendant 487 Possible Defenses Against an Embezzlement Charge 490 The Economics of Crime Model 492 Internal Controls 493 Fraud Risk Assessments 495 Detective Controls 496 Crime Insurance 498 Fraud Detection Methods 500 Other Fraud Prevention Methods 501 Final Words 504 Bibliography 507 Index 515"

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Author Information

MARK J. NIGRINI, PHD, is a professor at West Virginia University where he teaches highly-rated, graduate-level accounting technology and forensic accounting classes.He has published extensively in academic and professional journals on various topics related to forensic analytics. His current research addresses forensic and continuous monitoring techniques and advanced theoretical work on Benford’s Law.

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