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

Autor/inn/en	McCarthy, Richard; Ceccucci, Wendy; McCarthy, Mary; Halawi, Leila
Titel	Alpha Insurance: A Predictive Analytics Case to Analyze Automobile Insurance Fraud Using SAS Enterprise Miner™
Quelle	In: Information Systems Education Journal, 17 (2019) 2, S.20-24 (5 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1545-679X
Schlagwörter	Motor Vehicles; Insurance; Deception; Models; Predictor Variables + Suchen Sie Ihr Suchwort? Motor vehicle; Kraftwagen; Täuschung; Analogiemodell; Prädiktor
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™. Ultimately, the goal is to build an optimal predictive model to determine which of the automobile claims are potentially fraudulent. (As Provided).
Anmerkungen	Information Systems and Computing Academic Professionals. Box 488, Wrightsville Beach, NC 28480. e-mail: publisher@isedj.org; Web site: http://isedj.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01