1 Introduction


ORGANIZATIONS GENERATE AND RETAIN more information stored in electronic format than ever before, yet even though there is more analysis performed with the available data, fraud persists. With such vast amounts of data, abusive scheme transactions are hidden and are difficult to detect by traditional means. Data analytics can assist in uncovering signs of potential fraud with the aid of software to sort through large amounts of data to highlight anomalies.

This book will help you understand fraud and the different types of occupational fraud schemes. Specific data analytical tests are demonstrated along with suggested tests on how to uncover these frauds through the use of data analytics.


A short definition of fraud is outlined in Black’s Law Dictionary:

An act of intentional deception or dishonesty perpetrated by one or more individuals, generally for financial gain.1

This simple definition mandates a number of elements that must be addressed in order to prove fraud:

  • The statement must be false and material.
  • The individual must know that the statement is untrue.
  • The intent to deceive the victim.
  • The victim relied on the statement.
  • The victim is injured financially or otherwise.

The false statement must substantially impact the victim’s decision to proceed with the transaction and that perpetrator must know the statement is false. A simple error or mistake is not fraudulent when it is not made to mislead the victim. The victim reasonably relied on the statement that caused injury to the victim or placed him or her at a disadvantage.

It is intentional deception that induces the victim to take a course of action that results in a loss that distinguishes the theft act.

In addition to the employer suffering a financial or other loss, occupational fraud involves an employee violating the trust associated with the job and hiding the fraud. The employee takes action to conceal the fraud and hopes it will not be discovered at all or until it is too late.

The word abuse is employed when the elements for defining fraud do not explicitly exist. In terms of occupational abuse, common examples include actions of employees:

  • Accessing Internet sites such as Facebook and eBay for personal reasons.
  • Taking a sick day when not sick.
  • Making personal phone calls.
  • Deliberately underperforming.
  • Taking office supplies for personal use.
  • Not earning the day’s pay while working offsite or telecommuting.

There is an endless list that can fall under the term abuse, but no reasonable employer would use this word to describe any employee unless the actions were excessive. Organizations may have policies in place for some of these items, such as an Acceptable Internet Use Policy, but most would be considered on a case-by-case basis, as the issue is a matter of degree that can be highly subjective. There would unlikely be any legal actions taken against an employee who participated in a mild form of abuse.


In the data analysis process, “Detecting a fraud is like finding the proverbial needle in the haystack.”2 Typically, fraudulent transactions in electronic records are few in relation to the large amount of records in data sets. Fraudulent transactions are not the norm. Other anomalies, such as accounting records anomalies, are due to inadequate procedures or other internal control weaknesses. These weaknesses would be repetitive and will occur frequently in the data set. Sometimes, they would regularly and consistently happen at specific intervals, such as at month- or year-end. Understanding the business and its practices and procedures helps to explain most anomalies.

inlinedbox TYPES OF FRAUD

The Association of Certified Fraud Examiners (ACFE) in the 2012 Report to the Nations3 outlines the three categories of occupational fraud and their subcategories in Figure 1.1.

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