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Development of utility function for life insurance buyers in the Indian market

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Journal of the Operational Research Society

Abstract

Insurance as a financial instrument has been used for a long time. The dramatic increase in competition within the insurance sector (in terms of providers coupled with awareness for the need for insurance) has concomitantly resulted in more policy options being available in the market. The insurance seller needs to know the buyer's preference for an insurance product accurately. Based on such multi-criterion decision-making, we use a logarithmic goal programming method to develop a linear utility model. The model is then used to develop a ready reckoner for policies that will aid investors in comparing them across various attributes.

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Appendices

Appendix A

Model explanation with a sample data set

In this part we explain the method using a small sample. We assume that there are eight factors and the maximum rating a respondent can assign, say, is 100, and the minimum, say, is 1.

Table A.1 shows the sample data set. The first responder's score for the factors are 85, 90, 100, 100, 95, 100, 70 and 75, respectively. Hence the values of responder 1 (t=1) for attribute 1 and 2 (or i=1 and j=2) are a 12 1=85/90=0.944. Similarly, we can find that for responder 8, a 23 8=80/60=1.333.

Table 8 Sample data set

Based on the above mentioned method, we compute the a ij t matrix. We show part of the matrix in Table A.2.

Table 9 Part computation of the a ij t matrix of sample data

Then we again implement equations 3 and 5 by making the transformation and putting it up in the matrix equation. We take the optimal values of w i and V i from the Excel solver, and after putting those values, we compute the values of a ij t×(v i /v j ) matrix (Table A.3).

Table 10 Part computation of the a ij t×(v i /v j ) matrix of sample data

In the next table, we show the p ij t matrix by taking care of the fact

We show p ij t matrix in Table A.4.

Table 11 Part computation of the p ij t matrix

We do similar transformation from q ij t matrix and then we can compute the logarithmic values to get the objective function and the constraints.

We now show the optimal solution of the model with the normalized values in Table A.5.

Table 12 Optimal solution that has been used in the computation

Appendix B

Life insurance study questionnaire

Dear Sir/Madam

We are doing a study on Utility Function of Life Insurance Buyers in the Indian Institute of Management, Ahmedabad (IIMA). We would like to know the factors you would consider when choosing a life insurance policy. Filling this questionnaire should not take you more than 3 minutes.illustration

figure b

Following are some factors people consider while buying an insurance policy. Indicate the importance of each factor in influencing your decision to buy a particular life insurance policy. Indicate the importance on a scale of 1 (not at all important) to 9 (very highly important).illustration

figure a

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Dutta, G., Basu, S. & John, J. Development of utility function for life insurance buyers in the Indian market. J Oper Res Soc 61, 585–593 (2010). https://doi.org/10.1057/jors.2009.26

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  • DOI: https://doi.org/10.1057/jors.2009.26

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