Optimal Claiming in an Automobile Insurance System with Bonus-Malus Structure

1985 ◽  
Vol 36 (3) ◽  
pp. 239-247 ◽  
Author(s):  
J. Kolderman ◽  
A. Volgenant
Keyword(s):  
Automobile Insurance ◽  
Insurance System

Decision Problems in the Canadian Automobile Insurance System

1972 ◽  
Vol 39 (1) ◽  
pp. 79 ◽  
Author(s):  
Christoph Haehling von Lanzenauer
Keyword(s):  
Decision Problems ◽  
Automobile Insurance ◽  
Insurance System

Evidence-based Claims Adjudication of Traffic Injury Claims in Ontario: Shifting the Focus from Cost to Care

2020 ◽  
Vol 48 (2) ◽  
pp. 154-164
Author(s):  
Keshini Moodley ◽  
Carol Cancelliere ◽  
Robert Power ◽  
Pierre Côté
Keyword(s):  
Evidence Based Medicine ◽  
Clinical Evidence ◽  
Automobile Insurance ◽  
Evidence Based ◽  
Clinical Interventions ◽  
Insurance System ◽  
Traffic Injury ◽  
Based Medicine

Background.— In the Ontario automobile insurance system, claims adjusters decide whether to approve, partially approve or deny funding for clinical interventions submitted by healthcare practitioners. Typically, these decisions are made based on cost, without considering the evidence on the effectiveness and safety of the interventions. Objective.— Develop an evidence-based claims adjudication framework, which can be used by automobile insurers to integrate clinical evidence into claims adjudication. Method.— We adapted the evidence-based medicine framework developed by Sackett et al1 to develop a framework for evidence-based claims adjudication. Conclusion.— An evidence-based claims adjudication framework may help insurers make claim decisions that will promote recovery of individuals injured in traffic collisions and reduce claims costs. The effectiveness and implementation of the framework needs to be evaluated.

scholarly journals Controlling Automobile Insurance Costs

1992 ◽  
Vol 6 (2) ◽  
pp. 95-115 ◽  
Author(s):  
J. David Cummins ◽  
Sharon Tennyson
Keyword(s):  
Insurance Market ◽  
Personal Injury ◽  
Automobile Insurance ◽  
Auto Insurance ◽  
Insurance System ◽  
Price Increases ◽  
Insurance Costs ◽  
Insurance Price ◽  
Cost Factors ◽  
Profit Rates

We begin by providing an overview of the auto insurance system and the structure of the auto insurance market. We then turn to an analysis of the factors underlying the auto insurance price increases experienced in recent years. We find that the auto insurance inflation of the 1980s was caused primarily by increases in cost factors, especially inflation in the severity of personal injury claims. There is no persuasive evidence that increasing profit rates or expense loadings contributed to inflation in premiums. The paper concludes with recommendations for bringing costs under control.

ON THE MEDICAL INSURANCE SYSTEM IN JAPAN

1955 ◽  
Vol 6 (3) ◽  
pp. 177-179
Author(s):  
Jo Ono
Keyword(s):  
Medical Insurance ◽  
Insurance System

Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

2019 ◽  
Vol 64 (2) ◽  
pp. 53-71
Author(s):  
Botond Benedek ◽  
Ede László
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Naive Bayes ◽  
Insurance Industry ◽  
Naïve Bayes ◽  
Sql Server ◽  
Categorical Variables ◽  
Automobile Insurance ◽  
Price Determination ◽  
Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

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