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 ...