Impact of Automated Driving Technology on Japanese Automobile Insurance Market

2021 ◽  
Vol 92 ◽  
pp. 123-141
Author(s):  
Akio Hoshino
Keyword(s):  
Insurance Market ◽  
Automobile Insurance ◽  
Automated Driving

Prioritizing Risk-level Factors in Comprehensive Automobile Insurance Management: A Hybrid Multi-criteria Decision-making Model

2020 ◽  
pp. 097215092093228
Author(s):  
Zahra Shams Esfandabadi ◽  
Meisam Ranjbari ◽  
Simone Domenico Scagnelli
Keyword(s):  
Insurance Market ◽  
Insurance Companies ◽  
Risk Level ◽  
Automobile Insurance ◽  
Two Phase ◽  
Analytic Hierarchy ◽  
Factors Affecting ◽  
Risk Levels ◽  
Fuzzy Delphi ◽  
The Individual

An efficient risk-level prediction for newly proposed insurance policies plays a significant role in the survival of companies in the highly competitive insurance market. In Iran, risk assessment in comprehensive automobile insurance, which is a part of motor insurance, is only based on the vehicle attributes without proper consideration of personal and behavioural characteristics of driver(s). As a result, pricing is unfair in most of the cases and this can put insurance companies in an unfavourable financial position due to attracting high-risk drivers instead of low-risk ones. In this scenario, to identify and prioritize important factors affecting risk levels and to move towards a fair ratemaking, a two-phase process based on fuzzy Delphi method (FDM) and fuzzy analytic hierarchy process (FAHP) is proposed in this research. Additionally, similarity aggregation method (SAM) is applied to combine the individual fuzzy opinions of the surveyed experts into a group fuzzy consensus opinion. The results of this empirical study contribute to the insurance market of Iran by proposing appropriate weighting of the relevant risk factors to support stakeholders and policymakers for assessing risks more accurately, as well as designing more effective databases and insurance proposal forms.

Price Dispersion in an Automobile Insurance Market

1986 ◽  
Vol 94 (2) ◽  
pp. 418-438 ◽  
Author(s):  
Bev Dahlby ◽  
Douglas S. West
Keyword(s):  
Price Dispersion ◽  
Insurance Market ◽  
Automobile Insurance

scholarly journals Association Rules for Understanding Policyholder Lapses

2018 ◽  
Author(s):  
Himchan Jeong ◽  
Guojun Gan ◽  
Emiliano A. Valdez
Keyword(s):  
Strong Association ◽  
Rule Learning ◽  
Insurance Market ◽  
Purchase Behavior ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Technique ◽  
Before And After ◽  
The Face ◽  
Two Stages

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

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