Paper: arXiv 2409.01843

Authors: Oytun Haçarız, Torsten Kleinow, Angus S. Macdonald

Abstract

If individuals at the highest mortality risk are also least likely to lapse a life insurance policy, then lapse-supported premiums magnify adverse selection costs. As an example, we model ‘Term to 100’ contracts, and risk as revealed by genetic test results. We identify three methods of managing lapse surplus: eliminating it by design; disposing of it retrospectively (through participation); or disposing of it prospectively (through lapse-supported premiums). We then assume a heterogeneous population in which: (a) insurers cannot identify individuals at high mortality risk; (b) a secondary market exists that prevents high-risk policies from lapsing; (c) financial underwriting is lax or absent; and (d) life insurance policies may even be initiated by third parties as a financial investment (STOLI). Adverse selection losses under (a) are typically very small, but under (b) can be increased by multiples, and under (c) and (d) increased almost without limit. We note that the different approaches to modeling lapses used in studies of adverse selection and genetic testing appear to be broadly equivalent and robust.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 3.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a solid theoretical foundation using continuous-time Markov models and Thiele’s differential equations to derive novel analytical insights regarding lapse-supported insurance and adverse selection. However, it relies primarily on theoretical modeling and scenario analysis rather than empirical data validation or rigorous statistical backtesting.

Research Flowchart

  flowchart TD
    A["Research Question: How do lapse-supported premiums<br/>affect adverse selection in life insurance?"] --> B["Input & Assumptions:<br/>Term-to-100 policies, genetic risk data,<br/>and lapse surplus management approaches"]
    
    B --> C["Model Assumptions:<br/>Asymmetric info, secondary markets (STOLI),<br/>and lax financial underwriting"]
    
    C --> D["Computational Process:<br/>Simulate adverse selection & mortality risk<br/>across lapse modeling methods"]
    
    D --> E["Findings: Standard asymmetric info losses are small,<br/>but secondary markets amplify costs by multiples"]
    
    E --> F["Outcome: Lax underwriting & STOLI create unlimited losses;<br/>lapse-supported premiums magnify adverse selection"]