Journal of Risk and Insurance最新文献

We present a model of a longevity risk transfer market with different market players (primary insurers, reinsurers, and capital market investors) and investigate how market dynamics and the market players' roles evolve with progressing market saturation. We find that reinsurers' appetite for longevity risk is the key driver in the early stage of market development. Since diversification benefits with other businesses decrease with every transaction, the reinsurance market is intrinsically antimonopolistic. With the increasing saturation of the reinsurance sector as a whole, its competitiveness shrinks leading to rising expected risk-adjusted returns for capital market investors. We show that in a saturated market, reinsurers should assume the entire longevity risk from primary insurers, diversify it within their business mix, and subsequently pass on only specific (nondiversifiable) components of the longevity risk to the capital markets. Our findings provide valuable suggestions on how to make the best use of the market's limited risk absorption capacity.

We analyze insurance demand when insurable losses come with an uninsurable zero-mean background risk that increases in the loss size. If the individual is risk vulnerable, loss-dependent background risk triggers a precautionary insurance motive and increases optimal insurance demand. Prudence alone is sufficient for insurance demand to increase in two cases: the case of fair insurance and the case where the smallest possible loss exceeds a certain threshold value (referred to as the large loss case). We derive conditions under which insurance demand increases or decreases in initial wealth. In the large loss case, prudence determines whether changes in the background risk lead to more insurance demand. We generalize this result to arbitrary loss distributions and find conditions based on decreasing third-degree Ross risk aversion, Arrow–Pratt risk aversion, and Arrow–Pratt temperance.

Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary insurance claim data. Furthermore, we conduct a field experiment in cooperation with an insurance company to investigate the performance of each approach in terms of identifying new fraudulent claims. We derive several important findings. Unsupervised learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on different input information. Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.

Accurate pricing of crop insurance policies relies on forecasts of probability densities of crop yields. Yield densities are dynamic, time series data on yields are often limited, and yield data are spatially correlated. We examine linear pooling of potentially related, but almost surely misspecified, crop yield density forecasts. The pooled forecasts combine densities from other spatial units based on out-of-sample forecast performance. The pooled densities result in more accurate premium rates which can reduce incentives for adverse selection. The approach is applicable to any insurance setting where the statistical model for the loss variable is likely to be misspecified and the underlying data-generating processes are potentially related.

While the impact of an Internet-based sales strategy on sales performance has been well studied, there is little academic research that examines the impact of a mobile application (MA) sales strategy on the sales performance of insurers. Using a unique data set for term life insurance policies from a Chinese life insurer, we study the impact of implementing this strategy on insurance purchases. We find a significant growth in the insurance purchase quantity and somewhat lower growth in premiums received from new policies. This paper determines that this is due to improved channel accessibility and the cost reduction of the MA channel. Although sales of traditional distribution channels are cannibalized in the short term by the MA distribution strategy, this substitution effect does not persist in the long run. In addition, we find that this strategy reduces impulsive purchases.

This article compares expected pension default losses of employees and retirees before and after pension buyouts. The comparisons are made using a stochastic model calibrated with market data. The analysis shows that the lower protection level provided by the State Guarantee Association relative to that of the Pension Benefit Guaranty Corporation (PBGC) is a critical factor that explains the welfare reduction, or equivalently, larger expected pension default losses, of most retirees who become annuity holders in the buyouts. The analysis also shows that the employee welfare, or equivalently expected pension default gains or losses, depends on the continued PBGC protection and, critically, their employers' postbuyout default risk and pension funding status. Moreover, these employee welfare changes are quite different for the corporations included in this analysis. Our results suggest that welfare improvements depend on the PBGC and state insurance regulators' cooperation in protecting pension participants and supervising buyout insurers.