Earticle

초록

영어

More than 1 million automobile insurance repairs occur per year globally, and the related repair costs add up to astronomical amounts. Insurance companies and repair shops are spending a great deal of money on manpower every year to claim reasonable insurance repair costs. For this reason, promptly predicting insurance claims for vehicles in accidents can help reduce social costs related to auto insurance. Several recent studies have been conducted in auto insurance repair prediction using variables such as photos of vehicle damage. We propose a new model that reflects auto insurance repair characteristics to predict auto insurance repair claims through an association rule method that combines gradient descent and location information. This method searches for the appropriate number of rules by applying the gradient descent method to results generated by association rules and eventually extracting main rules with a distance filter that reflects automobile part location information to find items suitable for insurance repair claims. According to our results, predictive performance could be improved by applying the rule set extracted by the proposed method. Therefore, a model combining the gradient descent method and a location-based association rule method is suitable for predicting auto insurance repair claims.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Work
2.1. Automobile Insurance Claim Prediction Models
2.2. Association Rule Analysis
Ⅲ. Proposed Approach
3.1. Current Auto Insurance Repair Item Prediction Model
3.2. Insurance Claim Prediction Framework
3.3. Auto Insurance Repair Data
3.4. Association Rules Generated with theGradient Descent Method
3.5. Location-based Filters with Gradient Descent
Ⅳ. Experimental Design and Results
4.1. Experimental Design
4.2. Results
Ⅴ. Discussion and Conclusion

저자

• Seongsu Jeong [ Ph.D. Candidate, The Business Informatics, Hanyang University, Korea ]
• Jong Woo Kim [ Professor, School of Business, Hanyang University, Korea ] Corresponding Author

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