Alpha Insurance: A Predictive Analytics Case to Analyze Automobile Insurance Fraud Using SAS Enterprise Miner

Richard McCarthy, Wendy Ceccucci, Mary McCarthy, Leila Halawi

Research output: Contribution to conferencePresentation

Abstract

Automobile Insurance fraud costs the insurance industry billions of dollars annually. This case study addresses claim fraud based on data extracted from Alpha Insurance’s automobile claim database. Students are provided the business problem and data sets. Initially, the students are required to develop their hypotheses and analyze the data. This includes identification of any missing or inaccurate data values and outliers as well as evaluation of the 22 variables. Next students will develop and optimize their predictive models using five techniques: regression, decision tree, neural network, gradient boosting, and ensemble. Then students will determine which model is the best fit providing consideration of the misclassification rate, average square error, or receiver operating characteristic (ROC). Lastly, students will generate predictive scores for the claims and evaluate the result using SAS Enterprise Miner (TM). Ultimately, the goal is to build an optimal predictive model to determine which of the
automobile claims are potentially fraudulent.
Original languageAmerican English
StatePublished - Nov 3 2018
Event2018 EDSIG Conference - Norfolk, VA
Duration: Nov 3 2018 → …

Conference

Conference2018 EDSIG Conference
Period11/3/18 → …

Keywords

  • insurance fraud
  • case studies
  • predictive analytics
  • SAS
  • neural network
  • decision tree
  • data mining

Disciplines

  • Insurance
  • Numerical Analysis and Computation

Cite this