Abstract Economists commonly conceptualize and characterize risk through metrics such as expected values, variance, value‐at‐risk, and objective probability distributions. This paper reviews these economic approaches from the perspective of contemporary risk science, which offers a holistic and unified framework for understanding and describing risk. Drawing on foundational risk science concepts and principles, the analysis demonstrates how this broader viewpoint integrates uncertainties, knowledge aspects, and potential surprises (black swans), providing new insights for both general risk conceptualization and economic applications. The paper concludes by discussing implications for risk assessment and management, arguing that adopting a risk science lens enhances decision‐making, balancing different concerns.
Two colleges at the University of Illinois faced significant financial risk due to tuition revenue being concentrated among students from China and Hong Kong. In response, they designed and purchased a bespoke, multi-year, dual-trigger indemnity insurance policy covering tuition revenue losses from specified geopolitical and pandemic events, a risk for which no established insurance product existed. This paper documents the full lifecycle of that contract: the analysis of insurability, the iterative design of indemnity versus index structures, franchise versus conventional deductibles, and dual-trigger architecture, the institutional frictions of public-sector procurement and disclosure obligations, and the policy's performance when COVID-19 triggered a claim resulting in approximately $21 million in recoveries against cumulative premiums of approximately $1.5 million. We discuss how insurance theory shaped each structural choice and compare the indemnity approach with the World Bank's Pandemic Emergency Financing Facility bonds, which employed a parametric structure active during the same event. The experience shows that seemingly uninsurable systemic risks can be rendered contractible through careful structuring.
This paper examines social inflation in the US property and casualty (P&C) insurance industry, exploring its definition, origins, and key drivers. We highlight its evolution over time and growing significance in 2025, and we discuss potential strategies for insurers, reinsurers, and policymakers to mitigate its impact in the future. We conclude that more academic research is necessary to evaluate social inflation.
This paper addresses the impact of improved data analytics on the size and scope of an insurance firm when access to information is costly. The paper examines the case where policyholders have private and correlated information about the loss they suffered. When individuals have limited abilities, designing complete contracts becomes very complex and demanding. Fortunately, combining an intelligent organizational design (i.e., an informational hierarchy) and improved data analytics helps reduce the effective complexity of insurance contracts. As a result, better information gathering processes allow insurers to (1) reduce their overhead by reducing the number of middle managers, (2) increase their size, or (3) increase their scope.
This study examines whether underwriting methods-representation, medical examination, extra premiums, and coverage-effectively mitigate adverse selection using data from an insurance company. Regarding representation, we focus on statements disclosing pre-existing medical conditions. Our findings show that for insured individuals with claims, claim costs are higher for those with pre-existing conditions or who require medical examinations. These individuals are also charged extra premiums. However, their loss ratios do not significantly differ from those without pre-existing conditions or medical examination requirements, possibly due to extra premiums. A quasi-difference-in-differences (DID) analysis confirms that extra premiums significantly lower the loss ratio of high-risk insureds, indicating that extra premiums serve as an effective underwriting mechanism. Lastly, insured individuals with pre-existing conditions receive lower coverage than those without, a finding that contrasts with existing literature. Finally, using the method introduced by Chiappori and Salanie (2000), we find a statistically significant positive correlation between the residuals of coverage and claim costs (loss ratio), suggesting strong evidence of adverse selection. Overall, the evidence suggests that underwriting effectively mitigates adverse selection.
Certain events can trigger multiple insurance claims across different lines of business (LOB), requiring insurers to pay out several indemnities simultaneously. Examples include car accidents causing both vehicle damage, third-party liability, and personal injury, or natural disasters generating widespread losses. Solvency capital requirements (SCR) should account for the dependence of such claims, rather than treating them in isolation. In this study, we employ extreme-value copulas (EVC) to model the dependence structure of claims arising from single events across multiple LOBs within a real insurance company. We assess how this dependence influences capital requirements. Our results show that EVC outperforms elliptical and Archimedean copulas in capturing tail dependencies when evaluating LOB-specific Value-at-Risk (VaR). However, since insurers must absorb total aggregated losses, capital adequacy should reflect aggregation risk. In such cases, radially symmetric copulas (e.g., t-Copula or Frank) may be more appropriate. A review of SCR models used in Brazil, Europe, and the US reveals that existing frameworks tend to overestimate required capital and may be insufficient for extreme-event scenarios. The study contributes to the literature by showing the benefits of incorporating EVC into solvency modeling, and by identifying limitations in current regulatory approaches, as the procedure used to estimate tail dependence (the "data-cutting method"). We also recommend the development of internal models tailored to the specific risk profile of each insurer, promoting both financial resilience and competitive advantage.
This paper investigates the relationship between corporate opacity and policyholders' purchasing behavior in US property-liability insurers. We find that policyholders are more willing to purchase policies from less opaque insurers. In addition, the financial tail risk exacerbates the negative relationship between opacity and insurance purchasing behavior. The evidence shows that opacity significantly affects purchasing behavior in commercial lines but not in personal lines, suggesting that commercial buyers/risk managers and their brokers help prospective policyholders evaluate insurers' financial strength, stability, and claims-paying efficiency, thereby reducing information asymmetry. The evidence also shows that opacity does not significantly influence the purchasing behavior in personal automobile and homeowners insurance lines (the most tightly regulated guaranty fund lines). In other words, prospective policyholders of guaranty fund lines may not be incentivized to assess the quality of insurers' financial information.
We propose a new method to measure systemic risk in the global insurance sector by analyzing interconnectedness among firms under different market conditions. Using a semi-parametric approach that relies on the Spearman correlation and copula-based partial dependence, we assess relationships in relatively stable, extremely bullish, and extremely bearish markets. Our approach provides a more flexible and robust framework than traditional methods that rely on linear correlation and the multivariate normal distribution. We show that geographic proximity and shared stock exchanges drive interconnectedness, while the Russo-Ukrainian war had a notable impact on the sector's network under relatively stable market conditions.
The insurance market is increasingly adopting connected insurance offerings, such as telematics, wearables, and parametric products, enabled by Internet of Things (IoT) technologies. These innovations generate granular, observable risk data and support the delivery of preventive services. While connected insurance has gained research attention, the role of IoT-enabled prevention and its impact on insurance, in particular on demand, remain underexplored. To address this gap, we conducted a systematic literature review following the PRISMA guidelines. From 5764 records identified across databases, journals, and industry sources, we analyzed 56 academic and 18 practitioner studies. The results include a comprehensive analysis of how IoT expands risk prevention and insurance mechanisms, as well as the additional benefits and costs introduced to connected insurance solutions, such as discounts, rewards, real-time services, technology expenses, privacy loss, and cyber risks. This evolution makes the cost–benefit structure broader and more complicated and introduces new drivers of insurance demand, such as technology affinity and the willingness to share data. We argue that existing demand models should be revisited to reflect these dynamics and outline implications for insurers' evolving role, from risk financiers to partners in risk management.
Insurers face difficulties because modernity creates risks that are human-made, globally interconnected, and unpredictable. This leads to correlated, cascading claims, while insurance consumers increasingly view prices as unfair. These issues weaken trust just when the industry needs credibility most. Insurers must evolve around the principles of resilience, risk reconceptualization, and reinvention. Resilience involves building adaptive capacity before, during, and after shocks. Risk reconceptualization requires shifting to a view of probability that updates beliefs as new risks emerge. Reinvention demands transparent products that reflect how consumers actually perceive fairness and value. Insurers engage in a series of targeted audits of their assumptions about the way they operate and the design of their offerings. In their audits, they incorporate empirical evidence on how consumers internalize the language of risk. Insurers provide meaningful and substantial context for consumers to understanding how consumers' risk-related choices link to what consumers pay for insurance. Insurers who act now will earn the trust of the future and succeed. Those who stick to familiar strategies will find themselves in a market they no longer control.
Property and Casualty (P&C) products with a simplified loss settlement logic (SLSL) can reduce insurers' combined ratios by up to five percentage points, as they lower underwriting and claims administration costs. However, these products introduce basis risk for policyholders. As shown in the theoretical section of our paper, policyholders who use an exponential utility function tend to combine traditional indemnity insurance with SLSL-based insurance to mitigate this risk. In the empirical section, we investigate willingness to pay for SLSL-based insurance using a behavioral experiment involving a low sum insured. The experiment revealed no significant difference in willingness to pay between indemnity insurance and SLSL-based products. This could be for a number of reasons, including that participants do not fully understand basis risk and its implications, the convenience of insuring with an SLSL product outweighs basis risk, or they perceive products to be of similar value if basis risk is not severe. Insurers could test demand for SLSL insurance in a pilot scheme. However, they should proceed with caution to mitigate the risks of potential litigation and reputational damage.
This paper presents a teaching case designed to engage instructors and students in the study of risk management for real estate portfolios, with a focus on the growing impact of flooding in the United States. The case introduces a flood-adjusted property valuation (FAPV) model, which uses a probabilistic approach to estimate property values discounted for flood risk in flood-prone areas. The FAPV model is then applied to calculate the Loan-to-Value ratio, followed by a discussion on strategies for mitigating flood risk within the portfolio. The teaching case includes a complete teaching schedule and methodology, requiring only a foundational understanding of probability and basic Excel skills. By working through this case, students gain practical insights into real-world decision-making processes in risk management.
The complexity of risk management arises from the diverse and interconnected risks that organizations face, requiring comprehensive strategies and tools to effectively identify, assess, mitigate, and monitor them. As a critical organizational function, risk management facilitates risk-informed decision-making that balances risk-reward trade-offs, minimizes unnecessary exposure, and strengthens resilience in the face of uncertainty and disruptions. Given the inherent uncertainty of future events, the analysis of historical failures is essential to understand the root causes and consequences. This process helps ensure that lessons learned inform future innovations and strategic responses to similar challenges. The collapse of Greensill Capital is a notable example of the complex interconnections within the financial sector. The case reveals how the actions and decisions of various entities, including finance companies, investment banks, institutional investors, insurance providers, and regulators, collectively contributed to the Greensill's downfall. It illustrates systemic vulnerabilities that can arise within interconnected financial systems and the cascading effects of risk mismanagement. Through the case study, we examine a real-world failure, apply theoretical concepts, strengthen critical thinking skills, and gain valuable insights into effective risk management practices.
The case study method of learning has long been a teaching approach that uses a form of experiential learning to allow students to make the connection between classroom learning and real-world applications. Cases such as those published by the Harvard Business Review utilize detailed historical information, financial data, and other relevant data, while others are created to highlight an existing set of problems or issues that also support or enhance the traditional lecture. In this paper, we present a course concept that provides a framework for multiple learning experiences where case studies build on core knowledge to prepare the student for real-time experiential learning opportunities based on live interactions with experts during company visits. The case studies serve to bridge the gap between traditional lectures and company visits and are complemented by the FM risk management game, online quizzes, and pre-visit company research projects. While our paper is based on an Enterprise Risk Management course in a specific geographic market, we believe that the framework of online learning, case studies, and on-site experiential learning is applicable in different locations and settings.
We study the optimal dynamic strategy of representative agents who can invest in the financial market and sign an insurance contract to optimise the utility of intertemporal consumption and face the risk of long-term-care (LTC) expenses. The time horizon of the agent coincides with the stochastic death time, and the health expenditure risk takes the form of a jump Poisson process. The agent may hedge against this health risk by signing an insurance contract, on which we assume there exists a mark-up. We find a closed-form solution for the optimal consumption, the optimal portfolio, and the optimal insurance hedge. We show that the decision to purchase LTC insurance is more complex than what emerges from most insurance models. The proportion of LTC expenditure insured decreases with age. Our model predicts substitution between private coverage and savings as a means to finance LTC expenditure. In response to a health shock requiring an increase in LTC expenditure, the individuals sell their assets to keep up the level of consumption (the so-called "consumption smoothing" effect). Richer individuals dissave more than poorer ones. An increase in the interest rate has the same qualitative impact. The reduction in the mark-up, either due to increasing competitiveness or through public subsidies, is likely to increase the welfare of well-off/fit individuals, while an increase in the interest rate may reduce coverage in a very substantial way, an aspect that has been overlooked by the literature so far.
This teaching case explores the National Football League's (NFL) evolving risk management strategy in response to mounting concussion-related liability. As research increasingly links repeated head trauma to long-term neurodegenerative diseases, the NFL has faced a surge in lawsuits, insurance market withdrawal, and reputational challenges. Traditional insurers have grown wary of underwriting the league's liability risks, prompting the NFL to consider self-insurance as a long-term solution. This case examines the financial and strategic implications of that decision, drawing on concepts from insurance economics, enterprise risk management, and long-tail risks, and the role of corporate culture in shaping risk responses. Designed for students in insurance, risk management, or sports economics courses, the teaching case fosters discussion on how large organizations manage uncertainty when traditional risk-transfer mechanisms fail.
Risk management is an important aspect of property insurance sales, underwriting, and rating that is covered lightly in standard texts or publishers' materials. This case is based on the risk management considerations of a fictitious insurer within an often competitive and always high-stakes industry segment-the primary insurance market for property catastrophes. The case is primarily concerned with performance management and the inherent tension that the competitive business environment can add to the management of insurance sales, underwriting, and rating risk. This case engages students in risky decision making with the option to use a simulation game that can be restricted to one round and one class period or played over several rounds in or out of class time. The game simulates randomized loss occurrences (based on pseudo-realistic probabilities) and competitive market dynamics (also containing an element of randomness). The case requires basic knowledge of commercial property insurance rating and underwriting, catastrophe risk, insurer performance analysis (namely, loss ratios and the premium-to-surplus ratio), and risk preferences. Decision making under uncertainty-with an appreciation of the linkages between sales, underwriting, loss costs, and risk capital-is a key learning of the case analysis. Group decision-making is an element of the case, and as such a secondary learning is negotiation and conflict resolution. Usage of Excel for case analysis is necessary.
This paper examines the impact of internal tournament incentives on reserve management within the property‐liability insurance industry. We find a positive relationship between internal tournament incentives and reserve errors, suggesting that a larger tournament prize is associated with more conservative loss‐reserve management. In contrast to the literature on nonfinancial firms, we do not observe a positive association between tournament incentives and risk‐taking behavior or performance. The overall evidence indicates that vice presidents participating in internal tournaments prioritize strong financial health over performance. Moreover, the positive effect of tournament incentives on conservative reserve management is more pronounced for insurers with more volatile returns and a higher ratio of claim loss reserves to total liabilities. This effect attenuates for larger insurers, those underwriting long‐tail lines, and those operating in less competitive environments. Our findings also suggest that the Sarbanes–Oxley Act significantly influences executives' reserve behavior. Finally, we show that stronger board monitoring is associated with more conservative reserve practices in internal tournaments.
This teaching case examines a critical staffing decision faced by the underwriting division of AMERICAN Insurance Company ("AMERICAN"), a multi-line, multi-state property and casualty ("P&C") carrier. Following significant policy growth and recent implementation of a new core technology platform designed to enhance underwriting efficiency, Mr. Edward Lloyd, head of underwriting, must determine whether to hire additional underwriting staff. Students need to make a data-driven recommendation to help Mr. Lloyd make a decision. The case requires students to integrate foundational knowledge of P&C insurance carrier operations with practical considerations such as training lags and productivity gains from automation. Students will perform time-series analyses of realistic staffing and policy data using Excel to evaluate trends in policy growth and underwriting productivity. Designed for use in risk management, insurance, and business analytics courses, this case gives students hands-on experience with data visualization, regression analysis, and strategic decision-making integrated with insurance carrier functions.
