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Improving Automobile Insurance Repair Claims Prediction Using Gradient Decent and Location-based Association Rules

첫 페이지 보기
  • 발행기관
    한국경영정보학회 바로가기
  • 간행물
    Asia Pacific Journal of Information Systems KCI 등재 SCOPUS 바로가기
  • 통권
    제34권 제2호 (2024.06)바로가기
  • 페이지
    pp.565-584
  • 저자
    Seongsu Jeong, Jong Woo Kim
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A451951

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원문정보

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

키워드

Big Data Automobile Insurance Insurance Claim Prediction Association Rules Gradient Descent

저자

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

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국경영정보학회 [The Korea Society of Management information Systems]
  • 설립연도
    1989
  • 분야
    사회과학>경영학
  • 소개
    이 학회는 경영정보학의 연구 및 교류를 촉진하고 학문의 발전과 응용에 공헌함을 목적으로 합니다.

간행물

  • 간행물명
    Asia Pacific Journal of Information Systems
  • 간기
    계간
  • pISSN
    2288-5404
  • eISSN
    2288-6818
  • 수록기간
    1990~2026
  • 등재여부
    KCI 등재,SCOPUS
  • 십진분류
    KDC 325 DDC 658

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