A Case Study Using Data Analytics to Detect Hail Damage Insurance Claim Fraud

Abstract

Employers require that accounting students think critically and use data analytics tools to gain valuable insights for forensic, tax, auditing, and advisory services. This case provides students with a hands-on learning experience using data analytics and encourages critical thinking. Students are tasked with using Alteryx and Tableau to prepare and analyze a fictitious storm dataset and insurance claims dataset to identify claims that may be suspicious. They create visualizations and spreadsheets that support their recommendation for further analysis. The learning objectives are: (1) develop student knowledge and ability to conduct data preparation through the “Extract, Transform, and Load” (ETL) process; (2) expand student knowledge of data analytics and fraud investigation; (3) provide students with practice in fraud investigation skills, including critical thinking and problem solving; (4) develop skills specific to data analytics and data visualization in accounting; and (5) develop effective oral and written communication skills.