Basic Audit Data Analytics with R

Abstract

Accounting professors, students and many auditors need readily available, non-proprietary training material on audit data analytics (ADA). This need was identified at the panel discussion on data analytics at the AAA meeting in San Diego (August 2017). Several attendees stated that they need materials (instructions, cases, data, and code) to first train themselves in the basics of ADA and then use the same materials to teach the topic in their classes. In addition, feedback from a survey contracted by the CPA Canada Audit Data Analytics Committee (Hampton and Stratopoulos 2016) indicated that, outside of the big four public accounting firms, there are relatively limited opportunities for training existing auditors in the use of data analytics. “Basic Audit Data Analytics with R” is intended to meet the need noted above. The training uses the software R because it is open-source (free) and it provides virtually endless possibilities to those who learn it. The cases, including practice problems, use comprehensive large data sets for an entire company accessed from the HUB of Analytics Education. Millions of data points are updated regularly. The primary learning objective is to provide trainees with capabilities required to perform entry level ADA. This means that - given a set of well-defined objectives and a reasonably clean data set - those who have successfully taken the training should have an understanding of how basic data analytics can be effectively applied in a aspects of a financial statement audit. These basics include: 1. Setting ADA objectives. Identify aspects of audit where the audit team can use data analytics tools to obtain audit evidence. 2. Data Understanding: Identify sources of data, collect and extract data, become familiar with data structure, identify data quality issues. 3. Data Preparation: Be able to clean and transform data to enable effective and efficient analysis. 4. Modeling: Explain the model underlying the ADA in plain English. 5. Evaluation: Leverage statistical and logical techniques to evaluate how valuable a model is, what has been found, and what to do with the results. 6. Communication and Documentation: Communicate and document the results of the ADA and use new insights obtained to help answer questions and solve problems.