Analyzing factors affecting risk aversion: Case of life insurance data in Korea

This research employs machine learning analysis on extensive data from a prominent Korean life insurance company to substantiate the insurance demand theory, which posits that insurance demand increases with risk aversion. We quantitatively delineate the traits of risk-averse individuals. Our study focuses on a cohort of 94,306 individuals who have filed insurance claims due to illness. To forecast prospective insurance consumers inclined toward additional purchases, we construct a predictive model using a machine learning algorithm. This model incorporates 19 demographic and socioeconomic factors as independent variables, with additional insurance acquisition as the dependent variable. Consequently, we uncover the distinctive characteristics of consumers predicted to acquire supplementary insurance products. Our findings reveal a significant association between the independent variables and the likelihood of purchasing additional insurance. Notably, 10 out of the 19 independent variables exert a substantial influence on additional insurance acquisitions. These characteristics encompass residence in rural areas, a higher likelihood of being female, advanced age, increased assets, a higher likelihood of being blue-collar workers, lower education levels, a greater likelihood of being married or divorced/separated, a history of cancer, and a predisposition for existing policyholders with prior subscriptions to actual loss insurance or substantial insurance contract amounts. Our study holds academic significance by addressing limitations observed in prior research, which predominantly relied on questionnaires to qualitatively assess risk aversion. Instead, we offer specific insights into individual characteristics associated with risk aversion. Moreover, we anticipate that Korean insurance companies can leverage these insights to attract new clientele while retaining existing members through predictive risk aversion analysis. These findings also offer valuable insights across a spectrum of disciplines, including business administration, psychology, education, sociology, and sales/marketing, related to individuals' risk preferences and behaviors.