We utilize an incentive-compatible experiment to examine the robustness of demographic, behavioral, and psychological effects on insurance purchases across three different insurance frames: a neutral frame, a pandemic life insurance frame, and a non-pandemic life insurance frame. We find that the effects of these individual characteristics on insurance purchases vary depending on the framing of the decision task while holding constant other parameter values (e.g., loss probability, loss amount). Thus, our results suggest that researchers should take caution when inter-preting demographic effects depending on the insurance context. Furthermore, our results suggest that different framings or contexts may be a possible explanation for the previously observed mixed findings on demographic effects. As our experiment took place during the COVID-19 pandemic, we also examine factors that influenced decisions at this time. We find that psychological factors such as the reported level of worry and the perceived chance of dying from COVID-19 are correlated with increased insurance purchases.
Prior research suggests that the Small Business Administration disaster loan program (SBADLP) disadvantages low socio-economic status (SES) and minority groups. However, those same groups are least likely to have pre-existing disaster insurance, so should be more likely to report losses and receive approvals. The analysis here uses county-level data for 2003-2019 on disaster losses and loan approvals, various objective factors, and SES and race/ethnicity. A method used to study wage discrimination is applied, with reported losses and loan approvals predicted by objective factors in first-stage regressions, and the residuals regressed against SES and race/ethnicity variables. To account for non-linearities, quartile loss subsamples are analyzed. The first-stage regressions find various objective factors playing distinct roles across the loss quartiles. Given collinearity, the residuals regression results are discounted in favor of simple correlations, which link low SES and losses as well as approvals, consistent with high SES types having insurance, with mixed results for racial/ethnic minorities. However, for the least severe storms, low SES is negatively associated with reported losses, and approvals are negatively related to African Americans, Asian Americans, and Hispanics, suggesting that improvements in the training of (largely temporary) SBA employees working at disaster sites are warranted.
We analyze whether an insurance firm's organizational form affects the degree of earnings quality in the German property-liability insurance industry. Using a dataset of 1,856 firm-year observations for the years 2001-2021 and regression analyses, we examine the "demand" hypothesis versus the "opportunistic behavior" hypothesis to identify differences in the earnings quality of mutual and stock insurance firms. Our results indicate that, consistent with the opportunistic behavior hypothesis, mutual insurance firms show higher levels of earnings quality. The results hold for various measures of earnings quality and are not affected by macroeconomic condi-tions. Our findings are important for various stakeholders of insurance firms as they provide knowledge on the determinants of earnings quality, which supports improved consumer product decisions.
This study analyzes X (formerly Twitter) discussions on ChatGPT's role in the insurance industry using topic modeling. Using Brandwatch, we collect 5,203 posts from November 2022 to September 2023 with the keywords "ChatGPT" and "Insur-ance." After data cleaning, 1,446 posts are analyzed with BERTopic to identify five key themes: consumer perspectives, business applications, ChatGPT in healthcare insur-ance, its role in innovation, and ethical concerns. Findings reveal that ChatGPT is valued for simplifying insurance processes, improving customer experiences, and enhancing operational efficiency in areas like underwriting and claims management. However, challenges such as biases and ethical issues also emerge. This study offers insights into ChatGPT's evolving role in insurance and suggests avenues for future research on AI's impact in the industry.
Security executives, including Chief Information Security Officers (CISOs), are responsible for ensuring their organizations are protected against cyber threats. In the event that a data or network breach in their carefully constructed cyber defenses occurs, their responsibility becomes the mitigation of the impact. Cyber insurance offers a key new and novel tool in the mitigation of a cyber threat. In this research, we expose the resources and four Executive Evaluation of Cyber Insurance Carrier Pro-cesses (EECICP) cyber insurers employ. We then identify four recommendations for evaluating and choosing among potential cyber insurer partners. Finally, we take an evidence-based approach to evaluate these recommendations when deciding to estab-lish a strategic partnership with a cyber insurance carrier. We identify two novel aspects of cyber insurance-cyber underwriting and cyber claims handling-that cyber executives must understand. Our findings suggest that only by gaining clarity and fit in these areas are security executives able to make a choice of a carrier that maximizes their organization's overall resilience to an attack.
Systemic denials of health insurance coverage for medical nutrition therapy (MNT) in eating disorder (ED) treatment raise critical concerns about compliance with the Mental Health Parity and Addiction Equity Act (MHPAEA). Despite MHPAEA's 2008 passage and subsequent federal guidance, insurers continue to deny ED MNT claims in ways consistent with violations of law, policy, and medical standards of care. Economically, denial of low-cost outpatient MNT increases downstream costs, includ-ing emergency care, hospitalizations, and long-term complications as well as quality of life and loss of productivity due to the extensive time required for appeals. This pilot study, conducted by the International Federation of Eating Disorder Dietitians (IFEDD) Access to Care Project, analyzed 170 reported cases of improper denials and their subsequent appeals spanning 56 insurers and 26 states plus the District of Columbia. Analysis of attempts to reverse improper denials, including reconsideration requests, letters of medical necessity, peer-to-peer reviews, regulatory complaints, and hearings, showed that after one year only 7% of improper denials had been overturned, 14% of patients had resorted to paying out of pocket, and another 14% had discontin-ued medically-necessary care due to inability to pay. The majority of cases-65%- remained unresolved. This study provides the first formal dataset documenting a pattern of ED MNT denials inconsistent with MHPAEA regulations occuring across the US among multi-ple insurers. Findings highlight the urgent need for state and federal regulators to strengthen oversight, impose penalties, and ensure meaningful access to this essential component of ED treatment.
The readability of insurance policies has been a longstanding concern for regulators, insurers, and consumers. This study extends previous research by exam-ining the readability of personal auto and homeowners' insurance policies in Indiana. Using the Flesch and Flesch-Kincaid formulas, we analyzed 14 homeowners and 12 personal auto policies from major insurers. Our findings indicate that both types of policies are generally difficult to understand, with average Flesch scores below 45. While some sections, such as conditions and property coverage sections, of both policies were found to be more readable, others, like liability coverages, more specifi-cally uninsured and underinsured motorists coverage sections, were significantly more challenging. These results highlight the need for ongoing efforts to improve policy readability and ensure that consumers can make informed decisions about their insurance coverage
Life insurance, like other forms of insurance, relies heavily on large volumes of data. The business model is based on an exchange where companies receive payments in return for the promise to provide coverage in case of an accident. Thus, trust in the integrity of the data stored in databases is crucial. One method to ensure data reliability is the automatic detection of anomalies. While this approach is highly useful, it is also challenging due to the scarcity of labeled data that distinguish between normal and anomalous contracts or interactions. This manuscript discusses several classical and modern unsupervised anomaly detection methods and compares their perfor-mance across two different datasets. In order to facilitate the adoption of these methods by companies, this work also explores ways to automate the process, making it accessible even to non-data scientists.
The insurance industry is rapidly expanding and tightly interconnected with the healthcare, travel, and automobile sectors. Changes in any of these industries directly impact insurance, creating a dynamic environment. Consequently, there is a pressing need for a framework to help insurance companies navigate this dynamic landscape. This study employs a Modified Total Interpretive Structural Modeling (M-TISM) approach to identify critical aspects of uncertainty in the insurance industry and develop a framework to address them. The resulting interpretive framework offers a roadmap for handling uncertainties in various scenarios. Using the M-TISM model, policymakers can make informed decisions and adapt to changes with time. This framework enables the development of effective policies and practices that guide managers in adjusting corporate priorities, ultimately fostering economic and revenue growth. Furthermore, the study examines four key factors affecting the insurance sector: consumer awareness, innovation, after-sales services, and sales skills. Findings underscore the crucial role of digitalisation in enhancing consumer awareness and managing uncertainty. Insurance policies promoting digitalisation and innovation were found to improve customer satisfaction and competitiveness. Innovation in after-sales services was identified as vital for customer loyalty and differentiation while enhancing sales skills led to increased customer engagement and preferences. These insights provide valuable guidance for policymakers and insurance managers in navigating the evolving landscape of the insurance industry.
The research conducted during the pandemic offers valuable insights into the effectiveness of policies implemented to mitigate virus transmission and manage associated risks. This article highlights the implications of such research for risk management and insurance businesses, emphasizing the importance of incorporating these findings into their strategies, while offering a broader view of risk management The analysis encompasses various aspects, including the effectiveness of non-pharmaceutical interventions, the role of social capital and institutional quality, and the interplay between containment measures and economic support. By leveraging this research, insurance companies can enhance their understanding of pandemic risks, refine risk models, develop tailored insurance products, and establish effective risk management strategies for future health crises. Ultimately, this article provides a foundation for proactive risk management and insurance practices in the face of potential global health challenges, benefiting both the insurance industry and society at large.
This paper tries to explain the propensities of the U.S. population to seek a full dose of vaccinations against the COVID-19 pandemic. Beyond the consideration of vaccine dissemination at the disaggregated or the local level, the main focus of this study is on determining whether a lack of health insurance significantly impacted vaccination propensities. If it is indeed the case that a lack of health insurance mattered, this would be informative for policymakers since they tried to address this aspect in the vaccine rollout by subsidizing and offering vaccines at a zero price. Our results show that the uninsured were less likely to be fully vaccinated against the pandemic, and this finding holds across different modeling formulations. However, there were differences in the responses of the different population subgroups. The findings with respect to the vaccination propensities of the unvaccinated are noteworthy, especially significant given the fact the COVID-19 vaccines were made available free of cost to the public in the United States, irrespective of their insurance status. A policy lesson from these results is that perhaps a better outreach to communities of the uninsured to inform them about the costs and availability of the coronavirus vaccines would have been better. Interestingly, new covid cases did not significantly impact decisions to fully vaccinate, while greater prosperity made full vaccination more likely. We did not find robust evidence of the elderly having a greater propensity to be fully vaccinated. Finally, accounting for the political dimension, counties housing the seats of the state government had greater full vaccination rates, ceteris paribus.
In 2023 total health care spending in the US totaled approximately $4.7 trillion and represented 18 percent of GDP. In an attempt to reduce these expenditures, the Affordable Care Act (ACA) drastically reformed the operation and structure of health care and health insurance. We explore the effect the ACA had on health insurer liquidity by exploiting state-by-state variation and providing evidence that the ACA led health insurers to significantly adjust cash holdings. We find that for the 2010 to 2018 time period, health insurer cash as a proportion of assets increased by 40 percent and growth in cash was significantly greater than that of other types of insurers, but that specific ACA provisions had differing effects-Medicaid expansion, loss ratio regulation, and exchange participation were associated with reduced cash. Far from changing cash reserves for no reason, our empirical evidence suggests that health insurers altered cash as a precautionary strategic response to uncertainty created by the ACA.
In this study we examine how the passage of the Affordable Care Act (ACA) impacts health insurer capital structure and financing decisions. Economic theory suggests that a firm's capital structure depends on the institutional environment, including the regulatory environment. Using a panel of firm-level data on health insurers from 2004 to 2016, we first test whether insurer capital structure changed following the ACA. We then test whether specific provisions of the ACA influenced capital issuance. We find that the ratio of health insurer liabilities to capital significantly increased following the ACA. Furthermore, we observe that capital issuance determinants differ following the ACA and that this difference is influenced by medical loss ratio requirements, Medicaid expansion, and exchange participation. Our study contributes to the capital structure literature, as well as the literature examining the impacts of the ACA. Our study also has important implications for evaluating financial stability in the health insurance sector.
The developers of models for quantifying systemic cyber risk for the re/insurance industry have had little opportunity for their voice to be heard. Instead, the historical literature largely dismisses the sector as immature, inaccurate, and not up to the task of facilitating cyber re/insurance risk transfer. This characterization of the cyber modeling community may be true, but little evidence has been offered in support of such views. Further, no credible scholarly analysis of the effectiveness of cyber vendor models has been conducted. This article offers a first step in what hopefully can become a much richer and robust line of inquiry across the cyber re/insurance academic community. Consisting of qualitative research with cyber modeling vendor employees, this article offers a baseline view of how the modeling sector sees itself and its work with regard to the broader cyber re/insurance community. No such study of the model vendors themselves has been conducted. This article provides an opportunity for the modelers to say their piece in a sector that has largely overlooked their contributions
How does board ethnic diversity influence insurers' optimal risk-taking? Using a sample of property/casualty (P/C) companies, we document that boards with greater ethnic diversity make better decisions about risk: they implement less risky strategies without negatively affecting firm performance, effectively reducing the cost of risk to the organization. What board members' personal characteristics inherited from their ethnic background are associated with these effects? We find that the diversity of directors' levels of uncertainty avoidance linked with their ancestral country is the key driver of our results. Our findings align with theories proposing that diversity fosters moderation, leads to less idiosyncratic decisions, improves internal governance, and optimizes risk-taking.
Life assurance companies typically possess a wealth of data covering multiple systems and databases. These data are often used for analyzing the past and for describing the present. Taking account of the past, the future is mostly forecasted by traditional statistical methods. So far, only a few attempts were undertaken to perform estimations by means of machine learning approaches. In this work, the individual contract cancellation behavior of customers within two partial stocks is modeled by the aid of various classification methods. Partial stocks of private pension and endowment policy are considered. We describe the data used for the modeling, their structured and in which way they are cleansed. The utilized models are calibrated on the basis of an extensive tuning process, then graphically evaluated regarding their goodness-of-fit and with the help of a variable relevance concept, we investigate which features notably affect the individual contract cancellation behavior.
An insurance contract may be nonperforming—resulting in a situation in which the insured might be worse off than without insurance since also losing the premium. This study revisits how contract nonperformance risk influences individuals’ willingness to pay (WTP) for insurance contracts. In an incentive-compatible laboratory experiment, subjects state their maximum WTP for different insurance contracts, which only differ in their inherent nonperformance risk. While the median WTP for no-default contracts is above the actuarially fair premium, both the mere existence of default risk and an increase in default risk decrease participants’ median WTP below the actuarially fair premia.
In this study, we consider the impacts of insurers' default rates and prices on the demand function. We assume that consumers are risk-averse and that the market is transparent. We establish the objective of maximizing insurers' expected net profits. We obtain the optimal price, capital, default rate, and expected net profit. Our results show that the impact of the default rate in the demand function on optimal capital is important. In particular, for a single product line, the default rate and expected loss of insolvency are equal to zero for any capital cost rate. Therefore, solvency regulation is not required. For two product lines, it is important to significantly reduce the amount of capital to hedge against underwriting risk. We also obtain optimal solutions for two product lines under the constraint of solvency regulation.
This study examines whether a captive insurance subsidiary makes a positive impact on cash flow, using a dataset composed with the 2020 S&P 500 index constituents. The in-house, self-funding nature of a captive structure is expected to offer its parent company a potential upside of improving cash flow because recaptured premiums can be internally retained and efficiently invested. We find the determinants of captive formation include larger firm size, less cash holdings, better profitability, and dividend payments. Nevertheless, our analysis did not yield evidence that a parent company can assume risks through its captive subsidiary with an advantage of either improving its cash flow or smoothing its cash flow volatility. Overall, our results suggest that companies opt for captives over commercial insurance solutions to optimize managerial preferences and to expand corporate risk-financing options. [Key words: cash flow, captive insurance company/subsidiary/structure, risk financing, risk retention, alternative risk transfer, enterprise risk management] JEL classification: G22, G32, G41
Turkey presents a unique opportunity for improving insurance coverage, thanks to its dynamic economy, high working-age population, and strategic geopolitical location. Despite its significant economic potential, insurance penetration in Turkey is relatively low compared to other emerging nations. This study aims to identify the primary indicators and prioritize investment areas to improve Turkey's insurance penetration. The study employs the AHP method to weight selected criteria based on a literature review, relying on expert opinions. Subsequently, the TOPSIS method is used to rank the alternative results. The findings indicate that probability and level of competition are the most critical factors determining insurance coverage in Turkey. Additionally, technological transformation and intellectual capital are the most important investment areas to increase penetration in the country, while innovation is the least essential alternative. The results of our study can serve as a valuable reference point for industry stakeholders and policymakers, especially in economies struggling with low insurance penetration. This study presents a roadmap to narrow the insurance coverage gap by identifying and prioritizing strategic investment opportunities while optimizing investment returns. [Key words: Insurance coverage gap, insurance penetration, the Turkish insurance industry, investment prioritization.]
