ISSN 2394-5125
 

Review Article 


AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN.

Abstract
Every year, billions of dollars are lost in the auto insurance industry due to fraud, which forces insurance premium prices to go up annually. Although fraud detection solutions have been developed to fix the fraud detection problem, they all still face the same well-known problems of imbalanced data. There is need for a centralized claims database to gather a holistic view of fraudulent characteristic behavior. This paper proposes a data pre-processing technique, particularly a fraud behavior feature engineering approach, to improve the overall performance of prediction models. The behavior being assessed is be based on the RFM model along with an additional behavior analysis related to policy expiration. Furthermore, an ensemble feature selection and modeling is used to deal with the high dimensionality problems that the feature engineering approach brings along with it, as well as the class imbalance problems. The proposed approach shows a 56.2% increase in the F1-meaure, compared against the previous published stat-of-the-art results.

Key words: Fraud Detection, RFM Features; Ensemble Models, Random Forest, Feature Selection, Feature Engineering, Cost Sensitive Learning.


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by JOHANNES STEPHEN KALWIHURA
Articles by RAJASVARAN LOGESWARAN
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. JCR. 2020; 7(3): 125-129. doi:10.31838/jcr.07.03.23


Web Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. http://www.jcreview.com/?mno=90768 [Access: May 31, 2021]. doi:10.31838/jcr.07.03.23


AMA (American Medical Association) Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. JCR. 2020; 7(3): 125-129. doi:10.31838/jcr.07.03.23



Vancouver/ICMJE Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. JCR. (2020), [cited May 31, 2021]; 7(3): 125-129. doi:10.31838/jcr.07.03.23



Harvard Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN (2020) AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. JCR, 7 (3), 125-129. doi:10.31838/jcr.07.03.23



Turabian Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. 2020. AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. Journal of Critical Reviews, 7 (3), 125-129. doi:10.31838/jcr.07.03.23



Chicago Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. "AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH." Journal of Critical Reviews 7 (2020), 125-129. doi:10.31838/jcr.07.03.23



MLA (The Modern Language Association) Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN. "AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH." Journal of Critical Reviews 7.3 (2020), 125-129. Print. doi:10.31838/jcr.07.03.23



APA (American Psychological Association) Style

JOHANNES STEPHEN KALWIHURA, RAJASVARAN LOGESWARAN (2020) AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH. Journal of Critical Reviews, 7 (3), 125-129. doi:10.31838/jcr.07.03.23