Fraud and Fraud Detection
The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.
Fraud and Fraud Detection
F RAUD AND FRAUD DETECTION takes a data analytics approach to detecting anomalies in data that are indicators of fraud. The book starts by introducing the reader to the basics of fraud and fraud detection followed by practical steps for obtaining and organizing data in usable formats for analysis. Written by an auditor for auditors, accountants, and investigators, Fraud and Fraud Detection enables the reader to understand and apply statistics and statistical-sampling techniques. The major types of occupational fraud are reviewed and specific data analytical detection tests for each type are discussed along with step-by-step examples. A case study shows how zapper or electronic suppression of sales fraud in point-of-sales systems can be detected and quantified.
Any data analytic software may be used with the concepts of this book. However, this book uses CaseWare IDEA software to detail its step-by-step analytical procedures. The companion website provides access to a fully functional demonstration version of the latest IDEA software. The site also includes useful IDEAScripts that automate many of the data analytic tests.
Fraud and Fraud Detection provides insights that enhance the reader's data analytic skills. Readers will learn to:
Understand the different areas of fraud and their specific detection methods.
Evaluate point-of-sales system data files for zapper fraud.
Understand data requirements, file format types, and apply data verification procedures.
Understand and apply statistical sampling techniques.
Apply correlation and trend analysis to data and evaluate the results.
Identify anomalies and risk areas using computerized techniques.
Distinguish between anomalies and fraud.
Develop a step-by-step plan for detecting fraud through data analytics.
Utilize IDEA software to automate detection and identification procedures.
The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud-prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for experienced examiners. With data sets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.
HOW THIS BOOK IS ORGANIZED
This book is about identifying fraud with the aid of data analytic techniques. It includes some data analytical tests that you probably have not considered. It may also expose you to some useful features of IDEA and how to apply procedures to make you a more effective IDEA user. This is a list of the chapters in Fraud and Fraud Detection :
Chapter 1, "Introduction." This chapter provides a simple definition of fraud to distinguish it from abuse. Different types of frauds are outlined. The chapter includes a discussion of why a certain amount of fraud risk is acceptable to organizations and how risk assessments enable us to evaluate and focus on areas with a higher potential risk of fraud.
Chapter 2, "Fraud Detection." Occupational fraud is hard to detect as employees know their systems inside out. There are both fraudulent inclusions and fraudulent exclusions to be evaluated. This chapter discusses recognizing the red flags of fraud and different types of anomalies. Accounting and analytic anomalies are distinguished, as well as whether procedures are considered to be data mining or data analytics.
Chapter 3, "The Data Analysis Cycle." The data analysis cycle steps include evaluation, technology, and auditing the results from the analysis. Before you can do any analysis, you must have good data. This chapter defines the steps in obtaining the data, such