Multi-peril frequency credibility premium via shared random effects

Insurance (MIC: 8.2 SIC: 6331 NAIC: 524126)State Auto Financial is a property and casualty insurance holding company primarily engaged in writing both personal and business lines of insurance. Co. operates through its subsidiaries: State Auto Property and Casualty Insurance Company, Milbank Insurance Company, Farmers Casualty Insurance Company, State Auto Insurance Company of Ohio, State Auto National Insurance Company, Stateco Financial Services, Inc., Strategic Insurance Software, Inc. and 518 Property Management and Leasing, LLC.. Co.'s principal lines of business include personal and commercial automobile, homeowners and farmowners, commercial multi‐peril, workers' compensation, general liability and property insurance.

Artificial intelligence (AI) technologies are revolutionizing underwriting, claims processing, customer experience, and risk management. AI allows insurers to automate, be more precise, and tailor products. These advances also bring challenges, such as refreshing legacy systems, managing vast amounts of data, and protecting data. This chapter explores how AI may change the property and casualty (P&C) insurance business, including its benefits and drawbacks. In addition to operational issues, P&C insurance AI adoption has ethical issues. Ethical AI usage depends on algorithmic fairness, transparency, and equitable decision-making processes. Insurers must balance automation and human oversight to reduce risks and maintain customer trust. Insurers must navigate complex data protection laws and ethical guidelines as they adopt AI. The chapter also discusses the future of P&C insurance AI. Real-time data analytics, IoT integration, and AI-blockchain synergy can further transform the industry. AI will improve pricing models, catastrophe response, and insurance personalization as it continues to develop. Insurers must overcome AI adoption hurdles and prioritize ethics in their strategy in order to capitalize fully on these opportunities.

The global transition to net zero emissions presents a significant challenge and opportunity for the insurance industry. This paper explores how property and casualty (PC) insurers are adapting their business models to the realities of a changing climate. It examines the key challenges, including increased risk of catastrophic losses, changing regulatory landscapes, and evolving customer demands. The paper then analyzes a range of adaptation strategies, such as risk modeling enhancements, product innovation, proactive risk mitigation services, and sustainable investment practices. Through a proactive approach to climate risk management, these insurers can not only ensure their own long -term sustainability but also play a crucial role in enabling a smoother transition towards a low -carbon economy. PC insurers that successfully adapt to the net -zero landscape will reap significant benefits, including enhanced resilience, improved risk selection, and a competitive advantage in a sustainable marketplace.

In 2010, the Affordable Care Act was signed into law with various provisions taking effect over the following years, specifically the individual mandate in 2014. This reform drastically impacted the entire insurance industry, not simply health insurance. Many prior studies, however, focused only on how ACA impacted health insurance. Early estimates indicated that health insurers might transfer costs to property and casualty insurers, which would significantly impact the profitability of these companies. Despite the association of medical malpractice with healthcare, property and casualty insurers actually write the policies for medical malpractice incidents. Therefore, I chose to exclusively examine how the Affordable Care Act impacts the practices of property and casualty insurers that write medical malpractice policies. My study worked to answer three questions: (i) the change in the number of medical malpractice reports, especially in Medicaid expansion states, (ii) the change in the dollar amount of these claims, and (iii) the impact to the industry’s net income. Using statistical tests and regressions based on sample data of the number of medical malpractice reports and the inflation-adjusted dollar amounts of these claims in each of the 50 states from 2003 to 2018, healthcare consumption expenditures, and the number of individuals with health insurance, I concluded that the number of malpractice reports has increased and the dollar amount of claims has decreased since the enactment of the Affordable Care Act. However, only a portion of these changes can be attributed to the Affordable Care Act, as the additional number of people insured played a small role in the variations of the medical malpractice environment; rather, healthcare consumption expenditures substantially altered the medical malpractice statistics. Ultimately, I concluded that the changes that occurred in the medical malpractice claims after the passage and enactment of the Affordable Care Act had very little impact on the profitability of the property and casualty insurance industry.

This paper provides an overview of property and casualty insurance industry functions, insurance terminology descriptions, and special insurance coverage considerations for photovoltaic (PV) system owners, asset managers, operators, PV operation-and-maintenance service providers, utilities, and other parties. The paper includes the results of an analysis of 6 years of property and casualty insurance claims for PV equipment and discusses considerations for purchasing property and casualty insurance for owners and operators of PV systems. PV is a relatively new asset type, and insurance companies are revisiting rates and offerings as actuarial data become available. This paper seeks to inform decisions that optimize the balance of the cost of insurance with enabling benefits to finance, permitting, utility connections, and the operation of PV plants.

The property and casualty insurance sector is experiencing profound technological transformation driven by digital innovations that fundamentally restructure traditional insurance models. This technical review explores how cloud-native architectures, artificial intelligence, telematics, and regulatory technologies are reshaping core insurance functions. Cloud platforms with microservices architectures enable superior scalability while significantly reducing operational costs across the insurance value chain. AI-driven algorithmic underwriting systems transcend traditional actuarial models through ensemble learning techniques and deep neural networks, delivering enhanced risk assessment accuracy and processing efficiency. Telematics infrastructure with multiple data acquisition mechanisms has revolutionized auto insurance risk assessment, while RegTech solutions have transformed regulatory compliance through automated monitoring systems and blockchain frameworks. Autonomous vehicle insurance requires fundamentally different technical approaches including Bayesian networks and digital twin simulations, while advanced analytics architectures incorporate sophisticated security implementations such as homomorphic encryption and federated learning. Despite implementation challenges related to legacy system integration, data standardization, and model explainability, emerging directions including quantum computing, edge AI deployment, and decentralized insurance platforms promise to further evolve the technological landscape of property and casualty insurance.

The Development Mode of Chinese Property Insurance After Financial Crisis Period

* Moody's Preferred Stock Ratings for January 1982 show about 30 new issues of preferred stock during the preceding year, raising approximately $1 billion. Only four of these issues lacked a sinking fund. This is typical of the interval since December 1978, when a regulatory change made preferred stock having a sinking fund feature an attractive investment to property and casualty insurers, as well as an interesting financing alternative to some fund raisers. The emergence of sinking fund preferred (SFP) may be the result of a market segmentation: trade journals of the principal issuers, public utilities, claim that relatively low dividend yields are required on these securities, while trade journals of the principal buyers, property and casualty insurers, state that tax-adjusted rates of return are relatively high on SFP. This phenomenon merits a closer look. A recent note by Sorensen and Hawkins (S&H) 116], due to its brevity, only hints at several major issues in SFP financing (e.g., accumulators and seller motives). Their note shows that yield traits of SFP over a three-year period before a regulatory change of De-

Insurance (MIC: 8.2 SIC: 6331 NAIC: 524126)Harleysville Group underwrites property and casualty insurance in both the personal and commercial lines of insurance. As an underwriter of property and casualty insurance, Co. has three segments which consisted of the personal lines of insurance, the commercial lines of insurance and the investment function. Co.'s personal lines of insurance include both auto and homeowners, while its commercial lines include auto, commercial multi‐peril and workers compensation. Co.'s business is marketed primarily in the eastern and midwestern U.S. through independent agents. As of Dec 31 2007, these insurance coverages were marketed through approximately 1,500 insurance agencies.

Returns Post Catastrophe Induced Seasoned Equity Offerings of Property and Casualty Insurance Companies

Insurance (MIC: 8.2 SIC: 6331 NAIC: 524126)Harleysville Group is an insurance holding company that engages, through its subsidiaries, in the property and casualty insurance business on a regional basis. Co. underwrites property and casualty insurance in both the personal and commercial lines of insurance. The personal lines of insurance include both auto and homeowners, and the commercial lines include auto, commercial multi‐peril and workers compensation. At Dec 31 2006, these insurance coverages are marketed primarily in the Eastern and Midwestern U.S. through about 1,500 insurance agencies.

For more than five decades insurance regulators have debated whether investment income earned on a property and liability insurer's capital and/or reserves should be considered in some direct fashion when rates are reviewed and justified. This article first presents a survey that reveals that a significant number of states have adopted policies that require investment income be so considered. The second part of the article presents the results of another survey--this one of insurance company executives involved in the investment function. They were polled as to what impact, if any, the direct consideration of investment income in rate-making would have upon their investment policies. The results of the survey suggest that the new rating approach might encourage insurers to switch their attention, at the margin, toward more equities. Such a consequence could well have significant regulatory importance. For over five decades the question of whether investment income should be directly brought into property and liability insurance rate-making has been disputed.' Generally, the insurance industry has been in oppositionquite successful opposition until the last decade. More recently, those favoring the incorporation of investment results in rate-making have enjoyed considerable success. In mid-1972, the writer polled the insurance commissioners of the fifty states on the question of investment income in rate-making. The results of the survey are in Table 1.2 George B. Flanigan, Ph.D., is Assistant Professor in the School of Business & Economics of the University of North Carolina at Greensboro. This paper was presented at the 1973 Annual Meeting of ARIA. 1 For both sides of the debate see Bob Hedges, Insurance Rates and Earnings Considered Together, Journal of Risk and Insurance, September 1969, pp. 455-461; and H. E. Curry, Investment Income in Property and Casualty RateMaking, Journal of Risk and Insurance, September 1969, pp. 447-453. 2The following question was asked: In the approval of rates for property and casualty insurance, does your office explicitly consider investment income from unearned premium or loss reserves? I refer only to the direct credit of investment income against underwriting needs as in Virginia, Maryland, recently New Jersey, and other states. If your state does consider investment income, when was such policy initiated? The results of this survey, as presented in Table 1, are also reported by the same author in Investment Income and Rate-Making, Annals of the Society of Chartered Property and Casualty Underwriters, June 1973, pp. 59-60.

This 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.

A flexible approach for risk aggregation is considered. The model consists of a tree structure, bivariate copulas, and marginal distributions. The construction relies on a conditional independence assumption whose implications are studied. A procedure for selecting the tree structure is developed using hierarchical clustering techniques, along with a distance metric based on Kendall's tau. Estimation, simulation, and model validation are also discussed. The approach is illustrated using data from a Canadian property and casualty insurance company.The Canadian Journal of Statistics43: 60–81; 2015 © 2015 Statistical Society of Canada

Returns of Post-Catastrophe induced SEO Conducted by Property and Casualty Insurance Companies