Jeremy Clopton, CFE, CPA, ACDA
Managing Consultant, Forensics and Valuation Services, BKD, LLP
As Bob Tie reminded us in his January Fraud Magazine "Special to the Web" column, “Devil in the details,” data analytics is an effective way to identify potential fraudulent transactions. More broadly stated, it is an effective way to identify potential fraud. Regardless of what business process we examine, if good data is available, there is no reason why it shouldn’t be analyzed. Using analytics for fraud detection commonly focuses on accounts payable, payroll, the general ledger and bank transactions. However, there are many other applications of analytics for fraud detection. One of the more interesting applications mentioned in the news lately is the detection of academic fraud.
As a college sports fan, the recent allegations of academic fraud related to student athletes at the University of North Carolina – Chapel Hill caught my attention. One of the latest articles, “Updated data on North Carolina scandal details bogus classes, suspect grade changes” in The News Tribune, discusses a release of new data about the scandal. The article included student counts, percentages of enrollment, quantities of grade changes and many other revealing statistics. While the article does not explicitly state the consulting firm used data analytics, there was clearly an analysis of a large set of data from multiple perspectives to identify unknown patterns.
As it should, this story is likely to catch the attention of most colleges and universities in the nation, with or without athletics programs. The key takeaway from this scandal: institutions of higher education need to incorporate analytics into their academic compliance monitoring. A sample of classes and a high-level review is not likely to provide the depth of analysis needed to identify an academic fraud of this nature. A 100 percent analysis of all available academic data is a much more effective approach. Analyzing class compositions, student athlete performance trends and grade revision frequencies are just a few of the analytics to consider incorporating into your current processes.
The data capture and retention practices of institutions of higher education provide a solid foundation of data for analysis. The rules and regulations institutions must follow to remain compliant provide an outline of expectations and the means to categorize certain activities as unusual. It is time for these institutions to embrace data analytics, harnessing the power of this data and the structure of the regulations, to take a proactive approach to detecting and preventing academic fraud.