Journal of Risk and Insurance最新文献

We develop a novel machine learning (ML) framework to estimate a surrender charge for variable annuities (VAs) with the balance between human behavior and rational optimality. Optimality accounts for insurers' potential losses from strategic surrenders by policyholders who attempt to take advantage of the market situation. However, policyholders sometimes need to surrender a VA because of sudden personal financial distress or a terminal illness. The literature contains contributions for these two surrender decisions separately, but we consider them simultaneously using ML. The ML framework is a Bayesian mixture of a deep optimal stopping rule based on potentially high-dimensional financial variables and a statistical model with historical data. This framework can help insurers and pension funds to set surrender charges and perform stress testing in ways that balance profits and social responsibility by incorporating policyholders' behavioral data.

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.

Motivated by recent experiences in economies adopting the defined-contribution pension system, we study public annuities in the presence of survival probability heterogeneity. It is found that the difference of annuitization-weighted and unweighted averages of survival probabilities is a useful measure of the severity of adverse selection. We then examine public annuities with a guarantee feature which bundles annuity income and bequeathable wealth components. We show that when the heterogeneity in survival probability is limited, the magnitude of guarantee proportion is irrelevant. On the other hand, an increase in the guarantee proportion mitigates adverse selection when the extent of heterogeneity is sufficiently large, because the share of annuity purchase by retirees with lower (resp., higher) survival probabilities is increased (resp., decreased). We also obtain a similar set of results for public annuities with nonescalating payments. The results have useful implications regarding the design of public annuities.

Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection (AD) to vehicles' trip summaries, we develop a method allowing to derive a “routine” and a “peculiarity” anomaly profile for each vehicle. To this end, AD algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared with the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared with trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve the classification.

Risk-adjustment policies, which transfer money from insurers with healthy consumers to those with sick consumers, are an important tool to contend with adverse selection in health insurance markets. While the steady-state properties of risk-adjustment have been studied extensively, there is less evidence on the transition phase of policy implementation. We study the introduction and removal of risk-adjustment at California Public Employees' Retirement System and show that these changes meaningfully impact premiums via plan differences in enrollee health status. Despite these premium differences, there is limited consumer resorting due to consumer inertia, though new active enrollees respond more fluidly. We show that, with inertial consumers, risk-adjustment changes have substantial distributional consequences, leading to worse outcomes for sicker consumers when removed and vice-versa when implemented. We estimate a model of plan choice with premium sensitivity, brand preferences, and inertia and use these estimates to study the interaction between risk-adjustment policies and the strength of inertia.

The Affordable Care Act requires insurers to offer cost-sharing reductions (CSRs) to low-income consumers on the marketplaces. We link 2013–2015 All-Payer Claims Data to 2004–2013 administrative hospital discharge data from Utah and exploit policy-driven differences in the actuarial value of CSR plans that are solely determined by income. This allows us to examine the effect of cost-sharing on medical spending among low-income individuals. We find that enrollees facing lower levels of cost-sharing have higher levels of healthcare spending, controlling for past healthcare use. We estimate demand elasticities of total health care spending among this low-income population of approximately −0.12, suggesting that demand-side price mechanisms in health insurance design work similarly for low-income and higher-income individuals. We also find that cost-sharing subsidies substantially lower out-of-pocket medical care spending, showing that the CSR program is a key mechanism for making health care affordable to low-income individuals.