Hae Kang Lee
Korea University · Economics, Econometrics and Finance
About the Lab
Professor Hae Kang Lee's research lab focuses on the intersection of behavioral economics, insurance risk, and health outcomes, with a particular emphasis on aggregate lapsation risk in the life insurance sector. The lab investigates how macroeconomic conditions and individual-level characteristics—such as income, health status, and age—affect policyholder behavior, especially during economic downturns. Using large-scale, proprietary datasets and advanced machine learning techniques, the lab develops actuarial models to assess the financial impact of health engagement programs, such as smoking cessation initiatives, and evaluates their net benefits to insurers. The research also explores the incentives and cost-benefit trade-offs of life insurers in promoting policyholder health as a risk management strategy.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
5We study aggregate lapsation risk in the life insurance sector. We construct two lapsation risk factors that explain a large fraction of the common variation in lapse rates of the 30 largest life insurance companies. The first is a cyclical factor that is positively correlated with credit spreads and unemployment, while the second factor is a trend factor that correlates with the level of interest rates. Using a novel policy-level database from a large life insurer, we examine the heterogeneity
We study aggregate lapsation risk in the life insurance sector. We construct two lapsation risk factors that explain a large fraction of the common variation in lapse rates of the 30 largest life insurance companies. The first is a cyclical factor that is positively correlated with credit spreads and unemployment, while the second factor is a trend factor that correlates with the level of interest rates. Using a novel policy-level database from a large life insurer, we examine the heterogeneity
Abstract Life insurance companies, as equity stakeholders in policyholders’ lives, have incentives to mitigate their health risks. I introduce a framework that enables life insurers to evaluate the financial viability of developing and implementing health engagement programs. By leveraging a proprietary big database of health and mortality information from a large U.S. life insurer, I use machine learning techniques to quantify the benefits and use a rational addiction model to calculate the cos
Research Areas
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