Proposed Models for Comprehensive Automobile Insurance Ratemaking in Egypt with Parametric and Semi-Parametric Regression: A case study

2022 ◽  
Vol 11 (1) ◽  
pp. 41-55
2019 ◽  
Vol 4 (2019/4) ◽  

This article discusses a decision both European Union Member States and states in the United States must make: whether to raise their compulsory automobile insurance minimum amounts. The authors review a case study from the United States, the Commonwealth of Pennsylvania, and conclude a proposed increase in the compulsory minimum amounts should pass the legislator. The purpose of compulsory automobile insurance is to compensate victims of automobile accidents. Due to inflation, the minimum amounts in Pennsylvania no longer compensate adequately. Moreover, the data do not support the contention that an increase in the minimum amounts will cause large increases in premiums and uninsured rates. The authors conclude that compulsory minimum amounts should be periodically reviewed, as they are in the European Union, and that arguments about large increases in premiums and uninsured rates should be subjected to a careful review based on data.


Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.


Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.


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