Goal programming to evaluate the profile of the most profitable insurers: an application to the Spanish insurance industry

Abstract

This paper applies a multiobjective goal programming (GP) model to define the profile of the most profitable insurers by focusing on 14 firm‐decision variables and considering different scenarios resulting from the exogenous change in interest rate and GDP per capita growth variables. We consider a detailed database of Spanish non‐life insurers over the period 2003–2012 taking into account two dimensions of insurers’ results: underwriting results and investment results. A prior econometric analysis is used to find out relevant relations among the variables. Next, a GP model is formulated on the basis of the relationships obtained. The model is tested in a robust environment, allowing changes in the coefficients of the objective functions, and for several scenarios regarding crisis/noncrisis situations and changes in interest rates. We find that having the stock organizational form, being an unaffiliated single company and maintaining low levels of investment risk, leverage, and regulatory solvency are recommended for result optimization. Growth and reinsurance utilization are not advisable for optimizing the results, whereas size should be positively emphasized even more in instability periods and when interest rates increase. The results also show that the optimal level of the diversification/specialization strategy depends on economic conditions. More specialization is advisable as negative changes in interest rates increase. However, we find that the optimal values of the diversification variable are higher for the crisis scenarios compared to the corresponding noncrisis scenarios, suggesting that diversification creates value in crisis. Further sensitivity analyses show the soundness of the conclusions obtained.

Bancassurance and the coexistence of multiple insurance distribution channels

“…The effect of organizational form on efficiency was controlled by including a dummy variable that is equal to one for mutuals and zero for stocks (see e.g. Cummins and Xie, 2013;Eling and Schaper, 2017;Gonz alez-Fern andez et al, 2020). Finally, we also considered the period since the financial crisis started (i.e.…”

Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data

“…e economic variables of policyholders are mainly reflected in monthly income and bank deposits [22]. In the era of big data, the domestic insurance industry should make rational use of cloud computing, develop accurate and targeted solutions on the basis of full analysis of policyholders (customers), and realize the innovation of insurance services.…”

Analysis of the Well-Being Levels of Students in Spain and Finland through Interval Multiobjective Linear Programming

“…This piece of information may be very valuable for educational policy makers, since they could define policies to promote the students' well-being with some knowledge of the possible impacts of such policies in the future. Previously, some papers have shown the potential of this combined methodology to analyze different socioeconomic situations (see e.g., [20,21]), including the use of different multiobjective optimization approaches to solve the problems obtained. For example, in [20], the problem was solved by means of goal programming, while in [21], an interval multiobjective programming approach was applied.…”

“…Previously, some papers have shown the potential of this combined methodology to analyze different socioeconomic situations (see e.g., [20,21]), including the use of different multiobjective optimization approaches to solve the problems obtained. For example, in [20], the problem was solved by means of goal programming, while in [21], an interval multiobjective programming approach was applied. Nevertheless, to the best of our knowledge, none of them uses interval multiobjective programming to compare the well-being situations of students in two different countries, as in our study.…”