Manual journal entry testing: Data analytics and the risk of fraud

Abstract

Big Data is revolutionizing the business world as it enables companies to discover valuable insight from within the vast volumes of data now available to companies. The data analysis skills related to Big Data are in high demand, yet many accountants lack the data analysis skills necessary to improve decision making within a company or increase the effectiveness and efficiency of an audit. This case provides you with a hands-on opportunity to utilize the data analysis skills that are in such high demand. You will harness the power of Big Data while performing a procedure that is required on all financial statement audits – an analysis of journal entries for potential red flags of fraud. The case is completed in two phases. First, in Phase I, you will learn how to address one of the primary challenges in the use of Big Data – “validating” the data – which ensures that the files are complete. Specifically, you will import and analyze client files in IDEA to determine if the manual journal entry file is complete or if any records are missing from the file. In Phase II, you will validate data from a second client then perform a battery of tests aimed at identifying potential red flags of fraud. To complete the second task, you will need to consider factors such as who recorded the journal entries, when the entries were created, the description provided (or lack thereof), and whether the entries were back-posted or out-of-balance. You will also learn how to use fuzzy matching and Benford’s Law.