Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations

  Forensic Analytics
 

 By Mark J. Nigrini, Ph.D 

 

Description: 

 
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.

 

Forensic Analytics: 

 

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

 

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

 


 
Product Details:
Copyright 2011
ISBN 978-0-470-64726-4
Hardcover, 463 pages
John Wiley & Sons Publishing
 

Table of Contents:

 

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