This case study, developed by the KPMG Master of Data and Analytics Program Team, has been designed to give instructors a meaningful way to introduce basic forensic accounting and related data and analytics concepts into the classroom. The purpose of this case study is to introduce students to a few different potentially fraudulent schemes (check tampering and payroll fraud) occurring at a hypothetical company. Their role is to emulate that of a staff professional on a forensic engagement team that has been hired to take a look at the company’s overall risk profile, as well as a few of their high-risk business processes and controls. Students will gain exposure to identifying red flags in data, analyzing the various data files, and concluding on high-risk transactions and next steps, applying their learned fraud/forensics concepts. In addition, students will further exercise their Alteryx and Power BI skills to transform and visualize the underlying data files. This exercise allows students to compare and contrast the two different data analytics platforms to decide which tool is better suited for a certain task, which is a valuable skill in the field.

Intended Audience: 

Use by undergraduate or graduate accounting faculty and is designed for students who have some basic knowledge or previous experience with fraud/forensic accounting concepts, Alteryx, and Power BI.

Materials Included: 

Case study provides students with background on the hypothetical client engagement, as well as the various input files that will be used in the completion of the case study exercises. Further, the case study illustrates important data analytics considerations. 

Learning objectives:

The learning objectives of this forensics case study are as follows: 

1.                Introduce students to hypothetical fraudulent client activity and important considerations when identifying red flags in the underlying data or company risk profile.

2.                Build on foundational data transformation and visualization skills by comparing different data analytics platforms.

3.                Analyze a company’s general ledger and sub-ledger data to identify anomalies in the financials that may be indicative of broader fraudulent activity.

4.                Consider next steps upon identification of high-risk transactions.

5.                Identify specific fraudulent schemes committed and consider how they could have been prevented and/or detected.