Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations

Discover how to detect fraud, biases, or errors in your data using Access or Excel
Every fraud or error changes your data in some way or another. The challenge is to find those needles in the haystack. Forensic Analytics shows how you can use Microsoft Access and Excel as your primary interrogation tools to find exceptional, irregular, and anomalous transactions.
Forensic Analytics reveals how you can:
Use Access, Excel, and PowerPoint in a forensic setting
Use statistical techniques such as Benford′s Law, correlation, and time–series analysis to detect fraud and errors
Detect financial statement fraud using one of several statistical approaches
Score divisions, locations, agents, or customers for fraud risk
Become the data analytics expert in your organization
With over 300 images, Forensic Analytics reviews and shows how twenty tests can be used to detect fraud, errors, or biases in your data. For each test, the original data is shown together with the steps needed to get to the final result. The tests range from thorough overviews to assess the reasonableness of data, to highly focused tests that give small samples of highly suspicious transactions. Some tests incorporate time and changes over time while other tests look for irregular patterns, excessively large amounts, or excessive duplications. The tests are relevant to any kind or size organization, whether small or large, nonprofit, or government–related.

Spis treści:
Preface.
About the Author.
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.
Index.

Nota biograficzna:
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.

Okładka tylna:
Discover how to detect fraud, biases, or errors in your data using Access or Excel
Every fraud or error changes your data in some way or another. The challenge is to find those needles in the haystack. Forensic Analytics shows how you can use Microsoft Access and Excel as your primary interrogation tools to find exceptional, irregular, and anomalous transactions.
Forensic Analytics reveals how you can:
Use Access, Excel, and PowerPoint in a forensic setting
Use statistical techniques such as Benfo rd′s Law, correlation, and time–series analysis to detect fraud and errors
Detect financial statement fraud using one of several statistical approaches
Score divisions, locations, agents, or customers for fraud risk
Become the data analytics expert in your organization
With over 300 images, Forensic Analytics reviews and shows how twenty tests can be used to detect fraud, errors, or biases in your data. For each test, the original data is shown together with the steps needed to get to the final result. The tests range from thorough overviews to assess the reasonableness of data, to highly focused tests that give small samples of highly suspicious transactions. Some tests incorporate time and changes over time while other tests look for irregular patterns, excessively large amounts, or excessive duplications. The tests are relevant to any kind or size organization, whether small or large, nonprofit, or government–related.