Prediction of Automobile Claims by Psychological Methods. A Case Study in Automobile Insurance

1968 ◽  
Vol 35 (1) ◽  
pp. 49 ◽  
Author(s):  
Charles F. Haner
Keyword(s):  
Automobile Insurance

scholarly journals Raising compulsory automobile insurance minimum amounts: a case study from the United States

2019 ◽  
Vol 4 (2019/4) ◽  
Keyword(s):  
United States ◽  
European Union ◽  
Union Member ◽  
The United States ◽  
Automobile Insurance ◽  
The European Union ◽  
Automobile Accidents ◽  
Member States ◽  
European Union Member States

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.

Implementing a Data Mining Solution for an Automobile Insurance Company

2011 ◽  
pp. 63-73 ◽  
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

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.

Implementing a Data Mining Solution for an Automobile Insurance Company

2006 ◽  
pp. 189-201
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

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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