While occupational fraud takes various forms, the result is always the same: The numbers generated by fraud cannot hold up to the unfailing logic of the accounting equation. If executives add false sales and accounts receivable to increase the company’s revenue, profits and cash always will be out of kilter. Technology advancements have allowed this “accounting equation” to be systemized into computer logic and applied to company data.1 Results of this logic could take the form of a simple matching of the human resource file to the accounts payable vendor master file or it could be an advanced neural network application focused on detecting money laundering schemes.
Whether simple or advanced, data analysis provides many benefits in the prevention, detection, and prosecution of fraud. On one hand, entities and their fraud examiners gain insight on 100 percent of transaction data versus more limited manual methods of selection. Also, this approach generally can be completed in less time than manual procedures because of the automation. Entities also gain improved business intelligence because the generated reports often lead to conclusions beyond just the occurrence of fraud.
Though advanced technology simplifies data analysis, few entities robustly use the new software tools to detect fraud. In this article, I would like to relieve the fears of frauds examiners and encourage them to incorporate systems that, I am sure, will become components of their routine audits and fraud examinations.
The following information and much more is contained in a longer paper I wrote, in conjunction with the Institute of Internal Auditors (IIA) Foundation, “Proactively Detecting Occupational Fraud Using Computers.” Download the document at www.theiia.org/ecm/iiarf.cfm?doc_id=4248. And beginning with the November/December issue of The White Paper I will write a column on practical ways to use data analysis tools.