Abstract:
Purpose: this paper aimed at finding the relationship between the numbers of purchase and the customer’sincome. The data mining tools were applied in the study to find those customers who bought more than one life insurance policy and represented the signs of good payments at the same time.Design/ methodology/ approach: in the present research the data mining tools were employed based on CRISP- DM methodology. The K-means algorithm was used for classification and the prediction was based on a proposed formula in Excel worksheet.Findings: the researcher extracted some simple rules to predict customers’ clusters through selecting the customers who bought more than one policy and filtering the income- bringer customers as the companies wouldbe able to use this prediction to change their strategies in relation to different customers.Originality/value: Utilizing data mining tools to classify different customers in life insurance and prediction based on the classification were new approaches of the study. There was not enough research and implementationin relation to the CRM and data mining in the insurance industry in Iran. Especially CRISP-DM methodologywas not used extensively enough in a life insurance investigation.
Machine summary:
The data mining tools were applied in the study to find those customers who bought more than one life insurance policy and represented the signs of good payments at the same time.
Findings: the researcher extracted some simple rules to predict customers’ clusters through selecting the customers who bought more than one policy and filtering the income- bringer customers as the companies wouldbe able to use this prediction to change their strategies in relation to different customers.
Originality/value: Utilizing data mining tools to classify different customers in life insurance and prediction based on the classification were new approaches of the study.
It is used in different fields like fraud detection, insurance claim patterns, premium pricing, insurance rate making and finding the customer value for the company (Smith et al.
Data mining has been used in insurance in many fields like fraud detection, selection of the sales agents, customer acquisition, finding claim patterns, and so on (Smith et al.
In step four the research is implemented and data mining is executed, the next step is to evaluate the model extracted and the last step is to prepare a complete report for the management (Maalouf et al.
The simple process model is presented in figure 2 Table 1: Attributes ranking based on the influence on the insured commitment Attributes used for insured commitment in payments Mean value extracted in Questionnaire in SPSS Policy duration به تصویر صفحه مراجعه شود.
An Analysis of Customer Retention and Insurance Claim Patterns Using Data Mining: A Case Study.