Rutgers University 3 credits Accounting
In recent years, audit analytics has drawn great attention in the accounting profession due to the increase in demand for enhancing audit quality by regulators, creditors, and investors. Many audit firms and internal auditors have applied audit analytics to their audit processes. In response to this trend, this course aims at introducing our students to the concept of audit analytics, the basic audit analytical tools, and the application of various analytical methods in both internal and external audit processes. Please note that this course mainly emphasizes the usage of statistics and the interpretation of results rather than the mathematics of specific tools or techniques; in other words, this course does not primarily focus on the technical aspects of analytical methods. However, the audit analytics course includes experiential learning (i.e., Hands-on Experience) with the use of Tableau and its Data Guide function for visual analytics (there is a group project), and the use of Alteryx for data transformation and analytics (there are six case studies to be completed). We also cover a variety of emerging technologies and analytics-based topics, as described below. This course includes the following: Audit Analytics Programming Languages and Low Code / No Code Tools Landscape, Introduction to Tableau & Alteryx, Foundations of Data/File Sources, Structures, and Types relevant to audit analytics, Applying Analytics Across the Audit Process – Converting data to information, converting information to actionable insights, Applying Rules-Based Analytics, Anomaly Detection Analytics, and Statistical Analysis, Visual Analytics and Overall Audit Risk Assessment, Individual Audit Scoping, Continuous Risk Assessment, Applying Analytics to Other Audit-Related Areas –Regulatory Compliance (FCPA, Pharma Off-Label Promotion), Audit Analytics for Substantive Testing, Generative AI & ChatGPT in Auditing (including ‘Audit Co-Pilot’, ‘Prompt Engineering’), Text Mining & Analytics in Auditing – Theory, Predictive Analytics in Auditing, Fraud Risk Analytic Models – Statistical Analysis using the M-Score and F-Score, Benford's Law (describing relative frequence distribution for leading digits of numbers in datasets), Earnings Management & Manual Journal Entry Testing and Intelligent Automation & Robotic Process Automation.
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