Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company

Mohammad Farhan Khan, Farnaz Haider, Ahmed Al-Hmouz and Mohammad Mursaleen
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Mohammad Farhan Khan: School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
Farnaz Haider: Department of Agricultural Economics and Business Management, Aligarh Muslim University, Aligarh 202002, India
Ahmed Al-Hmouz: Department of Computer Information Systems, Middle East University, Amman 11831, Jordan
Mohammad Mursaleen: Department of Medical Research, China Medical University Hospital, China Medical University (Taiwan), Taichung 40402, Taiwan

Mathematics, 2021, vol. 9, issue 12, 1-22

Abstract: Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

Keywords: consumer behaviour; life insurance; support vector machine; logistic regression; demographic factors; economic factors; psychographic factors; data-driven approach (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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