Application of share of choice model in insurance industry based on conjoint analysis and mathematical programming: A case study of automobile insurance in China

2012 ◽  
Author(s):  
Emmanuel Fragniere ◽  
Francesco Moresino ◽  
Yi Shen
Keyword(s):  
Mathematical Programming ◽  
Conjoint Analysis ◽  
Choice Model ◽  
Insurance Industry ◽  
Automobile Insurance

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.

Transit Shift Response Analysis Through Fuzzy Rule Based-Choice Model: A Case Study of Indian Metropolitan City

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Ashu S. Kedia ◽  
D. Sowjanya ◽  
P. S. Salini ◽  
M. Jabeena ◽  
Bhimaji Krishnaji Katti
Keyword(s):  
Choice Model ◽  
Fuzzy Rule ◽  
Response Analysis ◽  
Rule Based ◽  
Metropolitan City ◽  
Shift Response

Searching Dimensions and Directions for Digital Innovations Within the Insurance Industry

2021 ◽  
Vol 12 (2) ◽  
pp. 63-89
Author(s):  
Heini Hyttinen ◽  
Hannu Kalevi Kivijärvi ◽  
Anssi Öörni
Keyword(s):  
Insurance Industry ◽  
Principal Component ◽  
Knowledge Synthesis ◽  
Insurance Companies ◽  
Process Automation ◽  
Structured Interviews ◽  
Organizational Capability ◽  
Pension Insurance ◽  
Digital Innovation

Discovery of digital innovations is a key organizational capability for sustaining competitive advantage. Despite its importance, discovery of digital innovations is still ill understood. In this paper, the authors seek to provide a theory-based practice for digital innovation discovery. To meet this objective, they source the theories of knowledge and knowledge combination. Data for this case study were collected through semi-structured interviews and a quantitative questionnaire from three pension insurance companies. The data were analyzed by using principal component analysis and by constructing biplots based of the results. Two significant dimensions in the digitalization needs that guide knowledge synthesis were recognized: the importance of adopting the enabler and the volume of resources needed to adopt the enabler. A closer look at the enablers revealed that the most business-critical current digital business enablers for the pension insurance industry are business process automation, online services, and big data.

Women at work: a case study of the insurance industry

1985 ◽  
Vol 16 (4) ◽  
pp. 75-84 ◽  
Author(s):  
John Benson ◽  
Gerard Griffin
Keyword(s):  
Insurance Industry
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