Association Rules for Fraud Detection
James A. Tackett · Journal of Corporate Accounting & Finance · 2013
Abstract A recent study reported that businesses normally lose 5% of their revenues to fraud each year—with the median loss for occupational fraud set at $140,000. But finding fraud is tough: 87% of the perpetrators have never been previously charged with a fraud‐related offense. And budgetary cutbacks are another obstacle for internal auditors, who are continuously being asked to do more with less. Computerized records offer greater accuracy and efficiency, but investigators can find themselves drowning in a sea of digital data. So fraud investigators need a practical way of finding the proverbial “needle in a haystack” clue that will lead them to the perpetrators. The author suggests one solution: association rules (AR), a data‐mining technique that can help auditors find fraud when they are not sure where to search. He explains how auditors can use AR to find patterns and relationships that would otherwise go unnoticed. © 2013 Wiley Periodicals, Inc.