This paper proposes a theoretical insurance model to explain well-documented loss underreporting and to study how strategic underreporting affects insurance demand. We consider a utility-maximizing insured who purchases a deductible insurance contract and follows a barrier strategy to decide whether she should report a loss. The insurer adopts a bonus-malus system with two rate classes, and the insured will move to or stay in the more expensive class if she reports a loss. First, we fix the insurance contract (deductibles) and obtain the equilibrium reporting strategy in semi-closed form. A key result is that the equilibrium barriers in both rate classes are strictly greater than the corresponding deductibles, provided that the insured economically prefers the less expensive rate class, thereby offering a theoretical explanation to underreporting. Second, we study an optimal deductible insurance problem in which the insured strategically underreports losses to maximize her utility. We find that the equilibrium deductibles are strictly positive, suggesting that full insurance, often assumed in related literature, is not optimal. Moreover, in equilibrium, the insured underreports a positive amount of her loss. Finally, we examine how underreporting affects the insurer’s expected profit.
Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser ((2022) Journal of Machine Learning Research, 23(176), 1–42.) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, and regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications.
With recent epidemics such as COVID-19, H1N1 and SARS causing devastating financial loss to the economy, it is important that insurance companies plan for financial costs of epidemics. This article proposes a new methodology for epidemic and insurance modelling by combining the existing deterministic compartmental models and the Markov multiple state models to facilitate actuarial computations to design new health insurance plans that cover epidemics. Our method is inspired by the seminal paper (Feng and Garrido (2011) North American Actuarial Journal, 15, 112–136.) of Feng and Garrido and complements the work of Hillairet and Lopez et al. in Hillairet and Lopez ((2021) Scandinavian Actuarial Journal, 2021(8), 671–694.) and Hillairet et al. ((2022) Insurance: Mathematics and Economics, 107, 88–101.) In this work, we use the deterministic SIR model and the Eyam epidemic data set to provide numerical illustrations for our method.
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