Casualty Insurance Research Articles

Adverse Development Cover for Property & Casualty Insurers

Adverse Development Cover for Property & Casualty Insurers

The Insurance Performance Measure: Bringing Value to the Insurance Industry

The performance of the property and casualty (P&C) insurance industry has suffered in past years. Part of the industry's difficulties stems from its focus on premium generation, as measured by the combined or underwriting ratio, at the expense of overall operational performance. In this regard, the underwriting ratio is an incomplete measure of operating performance since it ignores two critical P&C operational functions: the investment return and reinsurance results. In this paper we present the Insurance Performance Measure (IPM), a comprehensive performance metric for the P&C industry. The IPM captures the three critical operational functions mentioned above in an overall performance measure. The measure is versatile in that it can be easily decomposed into critical value drivers in each area of operation. Thus, the IPM framework enables P&C managers to observe the interaction and trade-offs between the operational functions of premium generation, investment return and reinsurance results separately or as a group, and as they relate to the opportunity cost of policyholders' surplus.

Consolidation of the US Property and Casualty Insurance Industry: Is Climate Risk a Causal Factor for Mergers and Acquisitions?

Consolidation of the US Property and Casualty Insurance Industry: Is Climate Risk a Causal Factor for Mergers and Acquisitions?

Artificial Intelligence on Property and Casualty Insurance

The property insurance and casualty insurance sector are large insurance sector that protects or covers the stuff you own (like Home, car and trucks). The provider that provides or covers huge amount of coverage to properties (like cars, trucks, motorcycles and property), other constructions, accidents, vandalism, on theft etc. Casualty insurance is that the policy has the liability coverage to protect yourself if you are the one who is legally responsible for an accident that creates injuries to another party or creates damage to the other party’s appurtenances. The property and casualty insurance are combined together into one insurance product under a single policy. With the help of innovative technology growth, Property and Casualty Insurance Carriers are steadily using leading-edge technologies like Artificial Intelligence and Machine learning, to avoid an intermediary between the insurer and the customer and directly reaching to the customers and make sure to deliver prompt and accurate services without any interruptions at an affordable cost. Among many examples, we can use one being an example is utilized of Artificial Intelligence and Machine learning in the Property and Casualty insurance sector, which is leading towards to have a much greater number of use cases substantial as prevention of fraudulent claims, making fast and accurate business decisions, predictive data analytics, improve customer satisfaction and much more. Mainly this paper talks about emerging technologies like AI, ML which is used and adopted in Property Insurance and Casualty Insurance domain. Artificial Intelligence provides many benefits to insurance carriers, so carriers can improve their process to support their customers. What is an AI: “AI is all about how the digitals and humans can collaborate; how digital can take over tasks they can do much better and faster than human, so human can focus on what only he can do.”

Quantitative Model for Determining Factors Affecting Underwriting Profit Margin: Applied to Property & Casualty Insurance Companies

Quantitative Model for Determining Factors Affecting Underwriting Profit Margin: Applied to Property & Casualty Insurance Companies

Defense in Competition: Multimarket and Structural Effects of Firm-Specific Competition on Risk Transfer

This study extends current knowledge on competition and corporate defense by investigating how firms transfer risk in response to their firm-specific multimarket and structural competitive conditions. Drawing on the theory of multimarket competition, we propose an inverted U–shaped relationship between multimarket competition and risk transfer. We also propose that, for the sake of gaining a competitive edge, the extent to which a firm manages risk will tend to be opposite to that of its competitors. Last, we propose that egocentric industry concentration will moderate the relationship between multimarket competition and risk transfer besides its direct effect. Analysis of reinsurance usage in the US property and casualty insurance industry strongly supports our model and shows that firm-specific competitive conditions are salient to a firm's risk transfer level.

Model mortality rates using property and casualty insurance reserving methods

The chain-ladder model is the most widely used technique for property and casualty insurance to estimate unpaid claims, including incurred but not reported (IBNR) claims. Inspired by the reserving method, we first apply a distribution-free method (the chain-ladder model) and its variant and a distributional method (the lognormal model) to project future mortality rates. Next, to simulate mortality rates for more applications, we also propose corresponding stochastic versions associated with both the lognormal model and the variant of the chain-ladder model. Finally, we demonstrate numerical illustrations with mortality data from the Human Mortality Database for both genders of the US, the UK, and Japan. To compare the forecasting performances of the proposed three models and the other five models (the Lee-Carter model, the Renshaw-Haberman model, the Cairns-Blake-Dowd model, the M6 and M7 models), we adopt mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as metrics. Numerical illustrations show that the proposed three models overall outperform the other five models.

Estimating Spillover Effects in Property and Casualty Insurance Consumption

The determinants of insurance consumption have been extensively studied, yet spatial correlations are rarely considered within this framework. In this article, we discuss several channels through which spatial dependence may arise and test for spatial dependence using six years (2009–2014) of province, city, and firm-level data from China. Results suggest that spatial spillover effects are large and have the potential to seriously bias marginal effect estimates if neglected. Specifically, when regions experience similar changes in determinants, marginal effect estimates in spatial models are nearly 1.5 times that implied by aspatial models. Additionally, we investigate the sources of the spatial dependence and find that cross-border spillovers occur within and between firms.

Wastewater System Inflow/Infiltration and Residential Pluvial Flood Damage Mitigation in Canada

Pluvial flooding in urban areas is one of the most significant drivers of disaster loss in Canada. Damages during pluvial flood events are associated with overwhelmed urban drainage (stormwater and wastewater) systems. During the period from 2013 to 2021, Canadian property and casualty insurers reported approximately CAD 2 billion in personal property (residential) pluvial sewer backup claims during flood catastrophes. There has been growing interest in managing pluvial urban flood risk, notably through newly funded national programs focused on climate change adaptation. These programs have included the development of new guidelines and standards focused on managing the underlying factors contributing to urban and basement flooding. Inflow and infiltration (I/I) has received limited attention in the pluvial flood literature, however. Informed by significant engagement with practitioners in Canada, this paper provides a review of the issue of I/I into wastewater systems and its relation to pluvial flooding. The paper will address concerns related to private property engagement in I/I and urban pluvial flood reduction programs. Both improved technical standards and administrative support are needed to ensure that wastewater infrastructure is less susceptible to I/I over its lifecycle.

The impact of insurance on economic growth in Central and Eastern European Countries against other European economies

Purpose – The purpose of this paper is to assess the impact of the insurance sector on economic growth based on panel data for 31 European countries covering the period 2004–2019, with particular emphasis on the countries of Central and Eastern Europe. Method – Dynamic econometric models were used in the analysis (Generalised Method of Moments). An attempt was made to quantify the impact of the insurance sector on economic growth for selected indicators of activity in that sector (insurance density and market penetration) for 2004– –2019. Conclusions – Research using regression analysis has confirmed the importance of insurance activity (in total, life and property & casualty insurance) for economic growth in a group of 31 European countries. The analysis carried out for the group of CEE countries has not confirmed significant impact of any of the variables characterising the development of total insurance and life insurance markets on GDP per capita. However, a significant impact of property & casualty (P&C) insurance, expressed in terms of insurance density, has been confirmed. Originality/value/implications/recommendations – The results of the study may constitute a recommen-dation for supporting the further development of the insurance sector by formulating appropriate regulations and legal frameworks and by taking measures that can increase trust in the insurance sector and raise insurance awareness.

Optimal labor and capital utilization by financial firms: evidence from the German property and casualty insurance industry

Motivated by discussions whether financial firms use too much equity capital and labor, we analyze the input utilization in the German property and casualty (p/c) insurance industry in a newly constructed sample of firm-level data in the period 1956–2019. An earlier study for the U.S. (Cummins and Nini, J Financial Serv Res 21(1–2):15–53, 2002) finds that p/c insurers over-utilize capital. Compared to this study, our large historical dataset exhibits less heterogeneity and thus allows a cleaner identification of the impact of firm characteristics on input factor utilization. We show that German p/c insurers have substantial cost savings potential and that labor input is the main driver of inefficiency. We also document severe differences in firm characteristics driving the utilization of labor. Our results contribute to both the academic discussion on productivity and efficiency in financial services as well as to the political discussion on the future of work and capital regulation in this sector.

Digital Transformation in the Property and Casualty Insurance Industry

Digital Transformation in the Property and Casualty Insurance Industry

Outlier detection and online monitoring of event sequences arising in customer service processes with unknown event-types

In this article, we study sequences of events with unknown event types commonly arising in customer service processes, such as claim processing, and present a novel approach for detecting outliers and online monitoring of the progression of such sequences until termination. In our approach, we create a series of semi-parametric Cox PH regression models (called sub-models) pertaining to each event occurrence and then use their residuals to create a multivariate normal monitoring statistic. Owing to the benefits of Cox PH, the method can explicitly incorporate relationships to past events. Furthermore, the statistic is also adjusted for non-termination which makes it usable in the online setting. We demonstrate the efficacy of our method through a numerical study and application to a real-world data set obtained from a property and casualty insurance company.

Property Natural Catastrophe Risk Analysis in an Era of Climate Change: Some Financial Services, Investment, and Public Policy Implications

Environmental, social, and corporate governance issues have become a hot topic that many firms are grappling with, given the increasing level of investor and consumer focus on the sustainability credentials of their businesses and what they are doing to improve them. For example, major institutional investors are beginning to apply pressure to boards of directors to proactively address climate change–related issues. Climate change manifests through both transition risks (as we move to a lower carbon economy) and physical risks (due to physical changes in the climate), including changes in natural catastrophe (nat-cat) frequencies and severities. Property and casualty insurance companies have been intensely tracking their nat-cat property accumulations since Hurricane Andrew in 1992. This article profiles the basic mechanics of nat-cat modeling and discusses how these mechanics are being influenced by climate change and the trend of recent nat-cat activity. Implications of this output on insurance/financial services, the investment community, and policy advisors are then profiled. In sum, there seems to be ample time for these parties to address the physical risks of climate change on future nat-cat events; however, the implications of inaction could be severe. We hope they use the time wisely. <b>TOPICS:</b>ESG investing, real estate, risk management, tail risks, legal/regulatory/public policy <b>Key Findings</b> ▪ Many firms are grappling with environmental, social, and corporate governance issues given the increasing level of investor and consumer focus on the sustainability credentials of their businesses and what they are doing to improve them. ▪ Institutional investors are beginning to apply pressure to boards of directors to proactively address climate change–related issues. Climate change manifests through both transition risks and physical risks, including changes in natural catastrophe frequencies and severities. ▪ There seems to be ample time for insurance/financial services, the investment community, and policy advisors to address the physical risks of climate change on future nat-cat events; however, the implications of inaction could be severe. We hope they use the time wisely.

Risk sharing under the dominant peer‐to‐peer property and casualty insurance business models

AbstractThis paper purposes to formalize the three business models dominating peer‐to‐peer (P2P) property and casualty insurance: the self‐governing model, the broker model, and the carrier model. The former one develops outside the insurance market whereas the latter ones may originate from the insurance industry, by partnering with an existing company or by issuing a new generation of participating insurance policies where part of the risk is shared within a community and higher losses, exceeding the community's risk‐bearing capacity are covered by an insurance or reinsurance company. The present paper proposes an actuarial modeling based on conditional mean risk sharing, to support the development of this new P2P insurance offer under each of the three business models. In addition, several specific questions are also addressed in the self‐governing model. Considering an economic agent who has to select the optimal pool for a risk to be shared with other participants, it is shown that uniform comparison of the Lorenz or concentration curves associated to the respective total losses of the pools under consideration allows the agent to decide which pool is preferable.

The role of financial conditions in portfolio choices: The case of insurers

Many institutional investors depend on the returns they generate to fund their operations and liabilities. How do these investors’ financial conditions affect the management of their portfolios? We address this issue using the insurance industry because insurers are large investors for which detailed portfolio data are available, and can face financial shocks from exogenous weather events which help us establish causality. Among corporate bonds, for which we can control for regulatory treatment, results suggest that when Property & Casualty (P&C) insurers become more constrained due to operating losses, they shift towards safer bonds. The effect of losses on allocations is likely to be causal because it holds when instrumenting for losses with weather shocks. The change in allocations following losses is larger for smaller or worse-rated insurers and during the financial crisis, suggesting that the shift toward safer securities is driven by concerns about financial flexibility. The results highlight the importance of financial conditions in institutional investors’ portfolio decisions.

Tweedie double GLM loss triangles with dependence within and across business lines

We propose a stochastic model allowing property and casualty insurers with multiple business lines to measure their liabilities for incurred claims risk and calculate associated capital requirements. Our model includes many desirable features which enable reproducing empirical properties of loss ratio dynamics. For instance, our model integrates a double generalized linear model relying on accident semester and development lag effects to represent both the mean and dispersion of loss ratio distributions, an autocorrelation structure between loss ratios of the various development lags, and a hierarchical copula model driving the dependence across the various business lines. The model allows for a joint simulation of loss triangles and the quantification of the overall portfolio risk through risk measures. Consequently, a diversification benefit associated to the economic capital requirements can be measured, in accordance with IFRS 17 standards which allow for the recognition of such benefit. The allocation of capital across business lines based on the Euler allocation principle is then illustrated. The implementation of our model is performed by estimating its parameters based on a car insurance data obtained from the General Insurance Statistical Agency (GISA), and by conducting numerical simulations whose results are then presented.

Awareness of climate risks and opportunities: empirical evidence on determinants and value from the U.S. and European insurance industry

In this paper, we study the awareness of European and U.S. insurance companies of climate-related risks and opportunities using a respective indicator from the Refinitiv Eikon database that uses reporting data. Based on this, we examine the determinants and value of the awareness of business risks and opportunities resulting from climate change, which, to the best of our knowledge, has not been done so far, despite its increasing and specific relevance for the insurance industry. We use a logistic regression analysis as well as a linear fixed effects model for a 10-year period from 2009 to 2018. Our results show that larger European insurers are significantly more likely to exhibit such awareness. When controlling for subsectors, property & casualty insurers tend to be aware of the risks and opportunities resulting from climate change. Moreover, when using the linear fixed effects model, we find a statistically significant positive value effect on Tobin’s Q.

Imprecise credibility theory

AbstractThe classical credibility theory is a cornerstone of experience rating, especially in the field of property and casualty insurance. An obstacle to putting the credibility theory into practice is the conversion of available prior information into a precise choice of crucial hyperparameters. In most real-world applications, the information necessary to justify a precise choice is lacking, so we propose animprecise credibility estimatorthat honestly acknowledges the imprecision in the hyperparameter specification. This results in an interval estimator that is doubly robust in the sense that it retains the credibility estimator’s freedom from model specification and fast asymptotic concentration, while simultaneously being insensitive to prior hyperparameter specification.

Analysis of Social Media Usage and Relationship to Profit Margin among Insurance Companies

Relationship marketing uses a strong relationship between seller and customer to offset limitations of selling an intangible service, such as insurance. Social media can facilitate interactions between agent and customer, thus strengthening the relationship. Customers willingly connect with companies via social media. This article examines the social media platforms’ usage of insurance companies. Findings show Facebook, Twitter, and LinkedIn to be most commonly used. Differences in social media usage were found between companies selling life and health insurance and those selling property and casualty insurance. Companies highly involved in social media possess higher profit margins than companies with low involvement.