Abstract
The growth of life insurance has been mainly depending on the risk of insured people. These risks are unevenly distributed among the people which can be captured from different characteristics and lifestyle. These unknown distribution needs to be analyzed from historical data and use for underwriting and policy-making in life insurance industry. Traditionally risk is calculated from selected features known as risk factors but today it becomes important to know these risk factors in high dimensional feature space. Clustering in high dimensional feature is a challenging task mainly because of the curse of dimensionality and noisy features. Hence the use of data mining and machine learning techniques should experiment to see some interesting pattern and behaviour. This will help life insurance company to protect from financial loss to the insured person and company as well. This paper focuses on analyzing hidden correlation among features and use it for risk calculation of an individual customer.
Highlights
The goal of life insurance as a monitory provision and protection from financial loss to the insured person helps the community
Proposed system work is focused on generating the null hypothesis (H0) to see how different features and their combinations are correlated with the risk factor
Segmentation was used in life insurance with the assumption that all individuals in the segment are “alike”
Summary
The goal of life insurance as a monitory provision and protection from financial loss to the insured person helps the community. Modern data mining can provide a useful technology to understand hidden knowledge which is useful to categories people purchasing insurance policy into the right risk group. This will ensure that company will not put the high-risk customer into a low-risk category and in effect, the company can take necessary action to enhance the loyalty of purchaser. Customer classification and clustering enable the firms to group similar customers together and help managers to better understand the customers’ needs; because it is much easier to identify and analyze the characteristics of groups of customers rather than studying each customer individually as suggested by the author After identifying these types of customers, the firm should motivate them to establish long-term relations. This review paper proposed a model to see and test the effects of high dimensional feature space in life insurance sector
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