Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations

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

With over 300 images, Forensic Analytics reviews and shows how twenty substantive and rigorous tests can be used to detect fraud, errors, estimates, or biases in your data. For each test, the original data is shown with the steps needed to get to the final result. The tests range from high-level data overviews to assess the reasonableness of data, to highly focused tests that give small samples of highly suspicious transactions. These tests are relevant to your organization, whether small or large, for profit, nonprofit, or government-related.

Demonstrates how to use Access, Excel, and PowerPoint in a forensic setting
Explores use of statistical techniques such as Benford's Law, descriptive statistics, correlation, and time-series analysis to detect fraud and errors
Discusses the detection of financial statement fraud using various statistical approaches
Explains how to score locations, agents, customers, or employees for fraud risk
Shows you how to become the data analytics expert in your organization

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

Preface.

Acknowledgments.

Chapter 1: Using Access in Forensic Investigations.

Chapter 2: Using Excel in Forensic Investigations.

Chapter 3: Using PowerPoint in Forensic Presentations.

Chapter 4: High-Level Data Overview Tests.

Chapter 5: Benford's Law: the Basics.

Chapter 6: Benford's Law: Assessing Conformity.

Chapter 7: Benford's Law: the Second-Order and Summation Tests.

Chapter 8: Benford's Law: the Number Duplication and Last-Two Digits Tests.

Chapter 9: Testing the Internal Diagnostics of Current Period and Prior Period Data.

Chapter 10: Identifying Fraud Using the Largest Subsets and Largest Growth Tests.

Chapter 11: Identifying Anomalies Using the Relative Size Factor Test.

Chapter 12: Identifying Fraud Using Abnormal Duplications within Subsets.

Chapter 13: Identifying Fraud Using Correlation.

Chapter 14: Identifying Fraud Using Time-Series Analysis.

Chapter 15: Fraud Risk Assessments of Forensic Units.

Chapter 16: Examples of Risk-Scoring with Access Queries.

Chapter 17: The Detection of Financial Statement Fraud.

Chapter 18: Using Analytics on Purchasing Card Transactions.

References.

About the Authors.

Index.

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.