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The article discusses the importance of cyber insurance for water utilities as part of a robust cybersecurity strategy. It highlights the complexity of selecting the right insurance coverage and provides a framework for determining the events that should be covered. The text emphasizes the need for organizations to understand the potential impacts of cyberattacks and to work with insurance experts to secure adequate coverage. The article aims to guide water utilities in analyzing their insurance needs and discussing the results with insurance brokers to ensure comprehensive coverage. [Extracted from the article]

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BACKGROUND AND OBJECTIVES: Fire insurance is a type of insurance that provides financial protection against property damage caused by events such as fire, theft, and weather-related damage. The use of modern analytics methods in analyzing policies and predicting potential losses is of great interest to insurance companies and those involved in property insurance services. Claim prediction is a crucial measure for predicting future losses in insurance companies, particularly in property insurance, where it helps evaluate the Probable Maximum Loss (PML) based on risk theory. METHODS: This study focuses on developing claim prediction models for occurrence probability, severity, and time by creating a dataset, training various algorithms, and comparing their performance. Exploratory Data Analysis (EDA) is conducted to select relevant features, resulting in the selection of 44 fields from insurance policy and claim information. Recursive Field Elimination (RFE) is used to reduce the dataset dimensions, and informative fields are selected for each prediction model. The dataset consists of over 780,000 policy records and approximately 70,000 loss payments from the Iran Insurance Company's actual fire application database spanning 10 years (2011-2021). Linear regression, random forest regression (RFR), support vector regression (SVR), and deep neural network algorithms are implemented for each prediction model. The accuracy of the algorithms is evaluated using mean squared error (MSE) and mean absolute error (MAE) values. FINDINGS: The results of the prediction models show that the deep multi-layer perception (MLP) algorithm performs the best. After hyperparameter tuning and multiple runs, the final MSE values are determined as 0.117 for occurrence probability prediction, 0.042 for loss severity prediction, and 0.106 for claim time prediction. The comparison of the innovative model results with test data demonstrates that intelligent models provide more accurate predictions. Accurate prediction is highly valuable for insurance companies as it allows them to mitigate financial losses. The use of deep learning in loss time prediction, in addition to severity and probability prediction, is a significant innovation in this research. CONCLUSION: Machine learning techniques, such as deep learning, can help insurance companies optimize their services with higher accuracy, strengthen risk management practices, and provide tools for better decision-making. The application of deep learning in loss prediction can replace complex and inaccurate manual processes in insurance, leading to advancements in risk management, reinsurance management, and improvement of fire insurance pricing. [ABSTRACT FROM AUTHOR]

The article focuses on the changes to the long-standing attorney fee and bad faith statutes in the context of property insurance policies. Topics discussed include the history, intent, and application of such laws by courts throughout the state of Florida, legislation altering these long-standing laws, and the potential retroactive application of the enacted laws to the existing contracts and claims.

In conditions of high competition and variability of economic conditions, timely insurance of business risks can help the enterprise to preserve its financial resources and ensure stability and security in the market. The article aims to systematize business risks by groups of losses and generalize and characterize the types of insurance policies that can be useful to the enterprise due to their manifestations. It was found that entrepreneurial risk in economic activity is a generalizing term for a group of risks that arise at various stages of the capital cycle as a result of the actions of the enterprise itself, competitors, counterparties, changes in the market situation, technological failures and other internal and external factors. Types of business risks are systematized. Four main groups of enterprise losses due to business risks were identified, with the selection of types of insurance policies that can be useful for enterprises in case of corresponding losses. Different conditions for insurance against cyber threats and liability for environmental damage in Ukraine were disclosed under four main costs depending on the type of damage subject to compensation. The results of the study show that business risk is a complex category that covers various aspects of business activity, protection against which occurs mainly within the scope of property insurance and liability insurance, but at the same time can go beyond these fields of insurance, if it is determined by the needs of business activity Business risk includes financial risks, such as market instability, changes in the economic environment, exchange rate fluctuations, rising costs, insufficient financial resources. It also covers operational risks related to business management, including supply chain issues, staffing issues, technology failures, legal conflicts, and more. [ABSTRACT FROM AUTHOR]

The article discusses topics about modern property insurance and gambling in the U.S. Also cited are a brief history of property insurance in the country, how climate change is affecting the insurance industry, and the parametric policies that are marketed as supplement to traditional insurance policies.

The subjects of the study are private security companies. Its object is the leading role of the customer-centric approach for financial and economic justification of the successful functioning and development of private security companies. The research hypothesis is that an objective assessment of the financial and economic aspects and the motivation of individuals and legal entities to purchase protection and security services under the conditions of post-epidemic recovery of the economy can be carried out by means of customer satisfaction and service pricing justification surveys. The aim of this article is to present the results of a customer satisfaction survey and the assessment of the financial and economic aspects as well as the motivation of individuals and legal entities to purchase protection and security services under the conditions of post-epidemic recovery of the economy. Structurally, the article includes three main sections: the first section focuses on the circulation of cash flows of private security companies; the second section presents selected elements of the conducted survey; and the third present an analysis of the survey among private security companies where the protection and security services compete and at the same time complement property insurance services. The survey results (based on a multi-crtieria Likert scale) confirm the validity of the "value for money" concept as an important factor for the pricing of private protection and security services. The survey questionnaire successfully compares the services of non-life insurance companies and the services of protection and security companies. The prevailing opinion is in favour of using the services of specialized companies over taking out insurance policies, with 61% of respondents expressing the opinion that hiring a specialized protection and security company is a more effective way to ensure property protection to taking out equivalent insurance with a general insurance company. [ABSTRACT FROM AUTHOR]

This article concerns deductible pricing in nonlife insurance contracts. The primary interest of insurers is the effect of the contract deductible on a policyholder's aggregate loss that is determined by a compound distribution where the sum of individual claim amount is stopped by the number of claims. Policyholders choose the deductible level based on their hidden risks, which makes deductible endogenous in the regressions for both claim frequency and claim severity. To address the endogeneity in the regression for the compound aggregate loss, we introduce a novel approach using pair copula constructions to jointly model the policyholder's deductible, number of claims, and individual claim amounts, in the context of compound distributions. The proposed method provides insurers an empirical tool to uncover the underlying risk distribution of the potential customers. In the application we consider an insurance portfolio from the property insurance program that provides property coverage for building and contents of local government entities of the Wisconsin. Using the historical data on policyholder and insurance claims, we first provide empirical evidence of the endogeneity of the deductible. Interestingly, we find that the policyholder's deductible is negatively associated with the claim frequency but positively associated with the claim severity. For the portfolio of policyholders, the endogenous deductible model provides superior prediction for 65% and 71% of policyholders for claim frequency and severity, respectively. The endogeneity of deductible shows significant managerial implications on insurance operations. In particular, the risk score suggested by the proposed method allows the insurer to identify additional profitable underwriting strategies which are quantified by the Gini indices of 0.22 and 0.13 when switching from the exogenous deductible premium and the insurer's contract premium, respectively. for this article are available online. [ABSTRACT FROM AUTHOR]

This article, titled "Sum Insured and Non-life Insurance," discusses the importance of determining the correct sum insured when purchasing an insurance policy. The sum insured is the basis for deciding the premium and the extent of indemnity in the event of a claim. The article explores the different dimensions of the sum insured in non-life insurance, including property insurance, insurance of person, liability insurance, and insurance of income. It emphasizes the need for careful consideration and serious thought when determining the sum insured to ensure adequate protection. [Extracted from the article]

We present the La Mobilière insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobilière. To preserve the privacy of La Mobilière customers, we propose the data aggregated at two geographical levels, based on the place of residence of the customer: postal areas and municipalities. For each geographical area, the data provides summary statistics on: (i) the demographic characteristics of the customer base, (ii) characteristics of vehicles insurance policies and (iii) characteristics of housing and building insurance policies. To assess the validity of the data, we investigate the temporal consistency of the data and the representativeness of La Mobilière customer base along several dimensions (total population, percentage of foreigners, etc.). We also show how the insurance data can reliably model the spatial patterns of socio-economic indicators at a high geographical resolution. We believe that the reuse of this data provides an opportunity for researchers to broaden the socio-economic characterization of Swiss areas beyond the use of official data sources. Measurement(s) private property insurance Technology Type(s) digital curation Factor Type(s) year • zip code • municipality Sample Characteristic - Location Switzerland [ABSTRACT FROM AUTHOR]

Definitions of war found in cyber insurance policies provide a novel window into the concept of cyber war. Mediated by market forces, changes in policy wording reflect shifting expectations surrounding technology and military strategy. Legal cases contesting war clauses probe state-formulated narratives around war and offensive cyber operations. In a recent legal case, an insurer refused to pay a property insurance claim by arguing the cause of the claim—the NotPetya cyberattack—constitutes a hostile or warlike action. To understand the implications, we build a corpus of 56 cyber insurance policies. Longitudinal analysis reveals some specialist cyber insurance providers introduced policies without war clauses until as late as 2012. Recent years have seen war exclusions weakened as cyber insurance policies affirmatively cover "cyber terrorism". However, these clauses provide few explicit definitions, rather they prompt a legal discourse in which evidence is presented and subjected to formal reasoning. Going forward, war clauses will evolve so insurers can better quantify and control the costs resulting from offensive cyber operations. This pushes insurers to affirmatively describe the circumstances in which cyber conflict is uninsurable. [ABSTRACT FROM AUTHOR]

Long-term care insurance (LTCI) has low acceptance by consumers due to cost, failure by consumers to face this risk, and deficiencies in the product. Part of the problem is that insureds want coverage against their long-term care expenses, but must bear the risks of predicting the number of years of long-term care coverage needs and future costs of long-term care itself, as well as rising premiums to keep the policy in force at the coverage level sought. This means a good share of the future long-term care risk is still borne by the insured, even after buying LTCI. Unlike property and liability, disability, and life insurance, where some portion of the pool incurs a loss in the current year, LTCI creates a pool where the losses shift to, and increase in, future years. This presents an imperfect match between risk shifting and risk financing. Hybrid products that combine long-term care benefits with annuities or universal life insurance in a well-integrated format may be more attractive combinations than stand-alone LTCI to meet the multiple finance and estate needs, income constraints, and risk allocations of many consumers. [ABSTRACT FROM AUTHOR]

A discrete-time option-pricing model is used to derive the "fair" rate of return for the property-liability insurance firm. The rationale for the use of this model is that the financial claims of shareholders, policyholders, and tax authorities can be modeled as European options written on the income generated by the insurer's asset portfolio. This portfolio consists mostly of traded financial assets and is therefore relatively easy to value. By setting the value of the shareholders' option equal to the initial surplus, an implicit solution for the fair insurance price may be derived. Unlike previous insurance regulatory models, this approach addresses the ruin probability of the insurer, as well as nonlinear tax effects. [ABSTRACT FROM AUTHOR]

Insights into fire risk mitigation behavior are crucial for developing a public-private integrated fire-resistant management strategy that motivates contribution from households to fire risk reduction. This research focus on the effects of both cognitive factors and risk attitudes on fire mitigation behavior. Two cognitive factors are considered: threat appraisal and coping appraisal in the Protection Motivation Theory framework. In particular, the independent influence of the six components of threat appraisal (perceived probability, perceived severity, and fear emotions) and coping appraisal (response efficacy, self-efficacy, and response cost) on fire risk mitigation behavior are examined. Three different types of fire risk mitigation behavior: maintenance and cautions, structural building measures, and fire insurance policy are considered. This study presents the data of a field survey among three ancient Chinese complexes. Results indicate that coping appraisal and risk attitudes are important variables in terms of risk mitigation behavior. In particular, response efficacy and risk attitudes significantly contribute to the models that explain fire risk mitigation behavior. These findings suggest that the effectiveness of risk mitigation measures should be focused on risk communication, and risk attitudes should be emphasized in estimating fire insurance demand. [ABSTRACT FROM AUTHOR]

The Indonesian Financial Services Authority (OJK) has instructed all insurance providers in Indonesia to apply a mandatory tariff for property insurance. The tariff has to be uniformly applied and the rule to set the maximum and minimum premium rates for protection against losses should be applied. Furthermore, the OJK issued the new rule regarding selfretention and domestic reinsurance. Insurance companies are obliged to have and implement self-retention for each risk in accordance with the self-retention limits. Fluctuations in total premium income and claims may lead the insurance company cannot fulfill the obligation to the insured, thus the company needs to conduct reinsurance. Reinsurance helps protect insurers against unforeseen or extraordinary losses by allowing them to spread their risks. Since reinsurer chargers premium to the insurance company, a properly calculated optimal retention would be nearly as high as the insurer financial ability. This paper is aimed at determining optimal retentions indicated by the risk measure Value at Risk (VaR), Expected Shortfall (ES) and Minimum Variance (MV). Here we use the expectation premium principle which minimizes individual risks based on their quota share reinsurance. Regarding the data in an insurance property, we use a bivariate lognormal (BLN) distribution to obtain VaR, ES, and MV, and a bivariate exponential (BEXP) distribution to obtain MV. The bivariate distributions are required to derive the conditional probability of the amount of claim occurs given the benefit has occurred. We find that first, based on fitting distribution, the use of bivariate lognormal distribution is suitable for the determination of retention and the value of MV is between VaR and ES. Second, the value of MV-BEXP is less than MV-BLN that have an impact on high reinsurance's value. [ABSTRACT FROM AUTHOR]

The article offers information on the builder's risk insurance, a type of first-party property insurance covering those with an insurable interest. Topics discussed include the insurance's coverage of a building during construction, the nature and purpose of the insurance, specification of the builder's risk insurance coverage in the insurance clauses of most construction contracts, policy forms for builder's risk coverage and completed value or reporting value basis.