Journal of Risk and Insurance最新文献

In many jurisdictions, insurance companies are prohibited from discriminating based on certain policyholder characteristics. Exclusion of prohibited variables from models prevents direct discrimination, but fails to address proxy discrimination, a phenomenon especially prevalent when powerful predictive algorithms are fed with an abundance of acceptable covariates. The lack of formal definition for key fairness concepts, in particular indirect discrimination, hinders effective fairness assessment. We review causal inference notions and introduce a causal graph tailored for fairness in insurance. Exploiting these, we discuss potential sources of bias, formally define direct and indirect discrimination, and study the theoretical properties of fairness methodologies. A novel categorization of fair methodologies into five families (best-estimate, unaware, aware, hyperaware, and corrective) is constructed based on their expected fairness properties. A comprehensive pedagogical example illustrates the implications of our findings: the interplay between our fair score families, group fairness criteria, and discrimination.

We investigate whether the price paid for insurance explains dishonesty in reporting an insurance claim. In our laboratory experiment, participants earn money in a real-effort task but risk losing some of this income through one of four randomly assigned, privately observed loss amounts. Before observing their loss, participants indicate their reservation price for insurance that pays an indemnity equal to their stated loss. Participants are insured if their randomly assigned premium is less than their stated reservation price. This mechanism provides data on each participant's consumer surplus from insurance. After receiving their cash earnings minus their assigned loss in private, participants report their loss. We find that the insured report modestly but statistically insignificant larger losses than the uninsured. Among the insured, we find no clear evidence that their reporting of excess losses increases in the randomly assigned price of insurance or decreases in the consumer surplus from insurance.

Many regulated health insurance markets with community-rated premiums rely on risk adjustment (RA) to mitigate insurer-incentives to risk select. However, insurers remain typically undercompensated for chronically ill enrollees. We use historical data on health spending and risk adjuster information to identify individuals undercompensated by the Dutch RA model of 2021 and find a selective group (1% of the population) with an average annual undercompensation of €6,050. We supplement the RA model with a risk sharing modality called high-risk pooling (HRP) to organize residual-based compensations towards insurers for the identified group to reduce the mean undercompensation to zero. The effects are evaluated on subgroups defined by chronic disease, finding a 42% reduction of their average undercompensation. Therefore, through compensating 1% of the population, the insurer-incentives to select against chronically ill individuals substantially diminish. These results are compared to outlier-risk sharing (reinsurance), proving HRP to be more effective at reducing selection incentives.

Subsidized insurance against extreme weather events improves affordability among households in at-risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at-risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood-prone properties. This fully offsets the negative direct effects of flooding on property prices, with high-income areas and high-value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution.