An important development in personal lines of insurance in the United States is the use of credit history data for insurance risk classification to predict losses. This research presents the results of collaboration with industry conducted by a university at the request of its state legislature. The purpose was to see the viability and validity of the use of credit scoring to predict insurance losses given its controversial nature and criticism as redundant of other predictive variables currently used. Working with industry and government, this study analyzed more than 175,000 policyholders’ information for the relationship between credit score and claims. Credit scores were significantly related to incurred losses, evidencing both statistical and practical significance. We investigate whether the revealed relationship between credit score and incurred losses was explainable by overlap with existing underwriting variables or whether the credit score adds new information about losses not contained in existing underwriting variables. The results show that credit scores contain significant information not already incorporated into other traditional rating variables (e.g., age, sex, driving history). We discuss how sensation seeking and self-control theory provide a partial explanation of why credit scoring works (the psycho-social perspective). This article also presents an overview of biological and chemical correlates of risk taking that helps explain why knowing risk-taking behavior in one realm (e.g., risky financial behavior and poor credit history) transits to predicting risk-taking behavior in other realms (e.g., automobile insurance incurred losses). Additional research is needed to advance new nontraditional loss prediction variables from social media consumer information to using information provided by technological advances. The evolving and dynamic nature of the insurance marketplace makes it imperative that professionals continue to evolve predictive variables and for academics to assist with understanding the whys of the relationships through theory development.

The interplay between longevity risk and health state transitions for retirees’ optimal annuitization decisions is investigated. Using a life‐cycle framework incorporating wealth levels, bequest motives, and consumption floors created by government subsidies, we examine how increased longevity in conjunction with an individual's health state transition process impacts annuity purchase decisions. Health state transition matrices are estimated from the Health and Retirement Survey (HRS) data. The effects of increased longevity on annuitization decisions are considered when longevity is both accompanied by increased time spent in healthier states (morbidity compression) or experienced by more time in unhealthy states (morbidity expansion). We find that retirees’ annuity demand is affected by wealth, initial health status, and expansion or compression of morbidity. Wealthier retirees have higher annuity demand when health shocks are considered, and increased longevity increases demand even more when retirees expect an expansion or slight morbidity compression. With health shocks and expectations of severe morbidity compression considered, the opposite effect might occur. Thus, an annuity can help retirees hedge health shock costs when slight compression or expansion of morbidity occurs. For retirees with lower wealth, the consumption floor provided by governmental subsidies will create a decreased propensity to annuitize.

The recent final rule on Medicaid managed care establishes the minimum medical loss ratio (MLR) requirement for Medicaid managed care and contains several provisions to strengthen delivery and payment reforms and improve efficiency and quality of care. Accordingly, this research examines the quality of Medicaid managed care and the effect of MLR and efficiency. The results show that, medical services efficiency has an insignificant (but negative) effect on the quality of care, which indicates that there may be room to improve medical services efficiency without significantly reducing the quality of care. The MLR does have a significantly positive effect on the aggregate quality ratings, however the magnitude of this effect is very small. This indicates that a minimum MLR requirement of 80% or 85% does not make a large difference on quality ratings.

Within the context of Medicare’s enunciated triple aims of better health, better care, and lower costs, we examine the effectiveness of medical loss ratio (MLR) on health outcomes of Medicare Advantage insurers. We simultaneously examine the effect of an efficiency measure for the insurer performance: medical service utilization efficiency (an assessment of how efficiently an insurer provides medical services). This research is based upon collection and integration of several data sources: health outcome data, financial data, and medical service utilization data. The assessment procedure employs a two-stage analytical approach: efficiency analysis followed by regressions. We quantify insurer efficiency using data envelopment analysis (DEA), which determines the relative efficiency of an insurer when the inputs and outputs can both be multivariate. We then run regressions with the dependent variables being functional health outcomes (“improving or maintaining mental health,” “improving or maintaining physical health,” and “improving or maintaining physical and mental health”) and health improvement efficiency (how cost-efficient the insurer is in improving functional health outcomes). Independent variables include MLR, medical service utilization efficiency, and a rich set of control variables. We find that neither MLR nor medical service utilization efficiency provides a good regulatory and evaluation indicator for stimulating/producing functional health outcomes. On the other hand, they do both significantly relate to health improvement efficiency, and hence are both reasonable regulatory and monitoring indicators for efficiently producing positive health outcomes. Our results suggest that to enhance health improvement efficiency, medical service utilization efficiency should be incorporated as a cost-efficient regulatory and monitoring indicator when evaluating Medical Advantage insurers.

A goal of risk management in construction is to minimize risk exposure and the total cost of risk for a project. To this end, there are a variety of market mechanisms available for transferring risk and/or the financial consequences of a risk realization (e.g., transfer the financial consequences of a risk to an insurance company or use contractual non-insurance risk transfers such as hold harmless agreements to allocate financial responsibility to another party). Unique characteristics of construction risks are examined along with a discussion of which of these risks are insurable and which are not. The advisable risk handling mechanism to use (insurance, non-insurance transfer, retention or self-insurance, or some other technique) is provided Both the construction firm and its client must anticipate potential undesirable event occurrence with initial project planning, and build both downside risk protection and resilience into its risk management strategy. Future emerging technological advances and their impact on construction risks are discussed.