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http://dx.doi.org/10.3745/KTCCS.2020.9.4.89

Accident Information Based Reliability Estimation Model for Car Insurance Smart Contract  

Lee, Soojin (한양대학교 전자공학과)
Kim, Aeyoung (한양대학교 ERICA 공학기술연구소)
Seo, Seung-Hyun (한양대학교 ERICA 캠퍼스 전자공학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.9, no.4, 2020 , pp. 89-100 More about this Journal
Abstract
In order to reduce the time and cost used in insurance processing, studies have been actively carried out to apply blockchain smart contract technology to car insurance. However, by using traffic data that is insufficient to prove accidents, existing studies are being exposed to the risk of insurance fraud, such as forgery and overstated damage by malicious insurers. To solve this problem, we propose an accident data-based reliability estimation model by using both various types of data through sensors, RSUs, and IoT devices embedded in automobiles and smart contracts. In particular, the regression model was applied in consideration of the weight estimation according to the type of traffic accident data and the reliability estimation model trained according to various accident situations. The proposed model is expected to effectively reduce fraud and insurance litigation while providing transparency in the insurance process and streamlining it is well.
Keywords
Blockchain; Reliability Estimation; Regression Analysis; Car Insurance; Smart Contract;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 H. Kim and H. Kwon, "Blockchain Adoption in the Korean Insurancee Industry: Review and Response," Korea Insurance Research Institute, 2018.
2 Financial Supervisory Service [Internet], http://m.fss.or.kr: 8000/insucop/"
3 kasko2go AG, "go2solution ICO White paper," 2018.
4 Flavio Bonomi, "Fog Computing and Its Role in the Internet of Things," 2012.
5 C. H. Mason and W. D. Perreault Jr., "Collinearity, Power, and Interpretation of Multiple Regression Analysis," Journal of Marketing Research, Vol.28, No.3, pp.268-280, 1991.   DOI
6 F. Lambertu, V. Gattescgu, C. Demartini, M. Pelissier, A, Gomez, and V. Santamaria, "Blockchains Can Work for Car Insurance: Using Smart Contracts and Sensors to Provide On-demand Coverage," IEEE Consumer Electronics Magazine, Vol.7, No.4, pp.72-81, 2018.   DOI
7 L. Bader, J. C. Burger, R. Matzutt, and K. Wehrle, "Smart Contract-Based Car Insurancce Policies," IEEE Globecom Wrokshops, 2018.
8 A. Ometov, V. Petrov, S. Bezzateev, S. Andreev, Y. Koucheryavy, and M. Gerla, "Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications," IEEE Network, 2019.
9 G. De La Torre, P. Rad, and K.-K. R. Choo, "Driverless Vehicle Security: Challenges and Future Research Opportunities," Future Gener. Comput. Syst., 2018.
10 I. olusi, E. Marks, and M. Hallowell, "Wearable Technology for Personalized Construction Dafety Monitoring and Trending: Review of Applicable Devices," Autom. Constr. 2018.
11 Wach, W., M. Gidlewski, and L. Prochowski, "Modelling Reliability of Vehicle Collision Reconstruction Based on the Law of Conservation of Momentum and Burg Equations," 20th International Scientific Conference TRANSPORT MEANS, 2016.
12 Y. Kim and H. Lee, "A Comparison Study for the Pricing of Automobile Insurance Premium Based on Credibility," Communications for Statistical Applications and Methods, Vol.17, pp.713-724, 2010.   DOI