Hasil untuk "Insurance"

Platon Promyslov

This paper establishes that the survival probability in the non-life Cramér--Lundberg insurance model with proportional investment is a classical $C^2$-solution of the associated integro-differential equation under minimal moment conditions: it suffices that the claim size distribution is continuous and possesses a finite moment of some positive order.

He Liang, Wu Menxin, Guo Anhong et al.

Extreme meteorological disasters such as droughts, floods, heat stress, and low-temperature frost damage are increasing in frequency, spatial extent and severity in the context of global climate change. Additionally, shifts in modern agricultural production systems and the emergence of new technologies such as artificial intelligence and big data present novel opportunities for the development of agricultural meteorological services. Convenient and accurate agricultural meteorological services can provide critical support for safeguarding food security and enhancing disaster prevention and mitigation efforts. To further enhance the application capability of national agricultural meteorological services, the new version of China Agricultural Meteorological Service System (CAgMSS3.0) is under development based on the existing CAgMSS2.0 framework and is integrated with Meteorological Big Data Cloud Platform (Tianqing) of China Meteorological Administration. CAgMSS3.0 utilizes Tianqing cloud servers for the deployment of its basic data and algorithms. Compared with CAgMSS2.0, several new modules are introduced, such as crop meteorological suitability index, annual agroclimatic evaluation and prediction, all-weather crop growth condition monitoring and analysis via optical and microwave remote sensing, agricultural meteorological disaster index, and grid-based agricultural meteorological disaster monitoring and prediction. Furthermore, CAgMSS3.0 has improved soil moisture monitoring and evaluation by integrating machine learning with multi-source data fusion. It also incorporates advanced meteorological forecasting technology for the occurrence and development of agricultural pest and disease, an interactive national-provincial agricultural weather prediction framework, and refined methods for agricultural climate zoning as well as agricultural meteorological disaster risk zoning. This system significantly enhances the operational capacity of national agricultural meteorological services through its application. Nevertheless, CAgMSS3.0 has some limitations. First, functional modules currently lack integration of global agricultural meteorological monitoring and forecasting components. Second, emerging domains such as climate quality monitoring and forecasting for agricultural products, as well as agricultural meteorological financial and insurance services require further development in the system. Third, the application of cutting-edge technology, especially AI-driven decision support in agricultural meteorology, remains undeveloped. Future iterations of agricultural meteorological service system are expected to be incorporated into a new-generation weather business integration platform structured around an "intelligent core". Meanwhile, a large-scale model based on "AI + mechanism model" will be developed for crop growth simulation and intelligent agricultural meteorological services. These improvements are anticipated to facilitate more efficient, accurate, and intelligent agricultural meteorological services.

Dimitrios G. Konstantinides, Charalampos D. Passalidis

In this paper we consider a multivariate risk model with common renewal process, while the logarithmic returns of the insurers investment portfolio, are described by a Levy process. In the two main results are established an asymptotic expression for the entrance probability of the discounted aggregate claims in some rare sets x A. This asymptotic expression highlights the multivariate linear single big jump principle in asymptotic behavior of these probabilities. In the first result, we are restricted in the case where the insurer makes risk free investments, and hence we consider a non-negative Levy process. We assume that the claim vectors follow a distribution from a class, introduced here, and represents a negligibly smaller subclass of multivariate subexponential distributions, since the additional requirement for positive lower Karamata index, looks as a mild condition. Further, we consider that the insurance and financial risks, satisfy a weak dependence structure. In the second result, we allow arbitrarily dependence between the two risks, and we assume that the distribution of their product, at each renewal epoch, belongs to the intersection of the class of multivariate subexponential positively decreasing distributions with multivariate dominatedly varying distributions. In this theorem we also permit risky investment, putting a condition to Laplace exponent of the Levy process. We also note that even in the special one-dimensional subcase the main results are new. Furthermore, we present two examples, where we demand only conditions for the marginal distributions of both risks and their dependence structure. Both examples, under the restriction on multivariate regularly varying distributions provide more explicit and elegant relations in relation with that established in the main results.

Norbert Dzadz, Maciej Romaniuk

The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute errors (MAE). Then, a direct algorithm to construct a fuzzy expert's opinion concerning such outputs is also considered.

Elisatris Gultom, Siti Rohani, Huta Disyon

Public trust in the Indonesian insurance business is shaken by the crisis that rocked multiple insurance companies. This study investigates insurance brokerage institutions’ involvement in Indonesia’s insurance activities and how to improve public trust. This research uses normative legal research by examining library materials or secondary data. In contrast, the nature of the research is analytically descriptive, depicting the existence of insurance brokerage institutions in the Indonesian insurance industry for further analysis to complete the picture of brokerage companies’ role in improving the industry. The research found that insurance brokers help insureds choose an insurance carrier, handle the claims process, and resolve claim disputes, making them crucial to Indonesian insurance. Brokers can gain public trust by increasing insurance literacy, transparency of insurance product information, helping consumers resolve insurance claims and advising the Financial Services Authority on insurance regulations. To gain public trust in insurance, the insurance industry should prepare a more effective and comprehensive set of regulations to prevent legal uncertainty and provide adequate legal protection to customers. At a micro level, every regulation prepared by the company can provide certainty. The company’s ethics and corporate governance policy is explicit.

F. Canepa, M. P. Repetto, M. Burlando

Abstract Downbursts are severe wind systems originating from thunderstorm clouds, and their strong horizontal outflows can pose serious hazards to natural and built environments. In the context of the activities of the European project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind‐safer and cost‐efficient structures—a comprehensive database of full‐scale downburst measurements was built. All records were acquired by bi‐ or tri‐axial ultrasonic anemometers installed in the main ports of the High Tyrrhenian Sea, namely Genova, Livorno and La Spezia, within the European projects ‘Wind and Ports’ and ‘Wind, Ports and Sea’. The very limited space and time structure of downburst outflows makes the available records in nature inadequate for developing models that could be used in the atmospheric science and engineering communities. The database described herein represents a step forward in attempting to fill this gap. The downburst nature of all events contained in the dataset was verified through detailed meteorological analyses, including comparisons with radar and satellite images and lightning recordings. The wind speed records associated with the events detected by the anemometric network are made publicly available through the online repository Zenodo and can be reused for multiple purposes. The dataset is expected to convey an important impulse towards the physical characterization and modelling of downburst winds and their codification into design tools for the assessment of wind loading and its effects on structures and infrastructure. Furthermore, it could serve as a promising, essential tool for researchers and risk‐related insurance companies.

Rima Kaafarani, Leila Ismail, Oussama Zahwe

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all metrics.

Emma Kroell, Sebastian Jaimungal, Silvana M. Pesenti

We study a reinsurer who faces multiple sources of model uncertainty. The reinsurer offers contracts to $n$ insurers whose claims follow compound Poisson processes representing both idiosyncratic and systemic sources of loss. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an entropy penalty. Insurers meanwhile seek to maximise their expected utility without ambiguity. We solve this continuous-time Stackelberg game for general reinsurance contracts and find that the reinsurer prices under a distortion of the barycentre of the insurers' models. We apply our results to proportional reinsurance and excess-of-loss reinsurance contracts, and illustrate the solutions numerically. Furthermore, we solve the related problem where the reinsurer maximises, still under ambiguity, their expected utility and compare the solutions.

Arthur Charpentier, François Hu, Philipp Ratz

The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective. In this article, we first investigate why predictions are a necessity within the industry and why correcting biases is not as straightforward as simply identifying a sensitive variable. We then propose to ease the biases through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications.

Bodak Olha, Koval Tetiana, Korchemna Iryna

The purpose of the article is to analyze the main tasks and peculiarities of activities of the Institute of Manuscript of the V. I. Vernadskyi National Library of Ukraine during the period of martial law in Ukraine. The research methodology is based on the application of analysis, synthesis and generalization, classification and systematization, induction and deduction, narrative, historical-typological, historical-problematic, historical-systemic and interpretive methods. Scientific novelty. The results of the work of the Institute of Manuscript of the V. I. Vernadskyi National Library of Ukraine in 2022 and the first half of 2023 are highlighted, problematic issues are outlined. Threats to the handwritten monuments in the Institute of Manuscript in the conditions of Russian armed aggression are analyzed and practical steps for their protection and preservation are highlighted. Conservation works in order to preserve particularly valuable fonds of the Institute of Manuscript were carried out. In June 2022, the Institute activities were resumed out remotely in the martial law conditions. The Institute of Manuscript of the V. I. Vernadskyi National Library of Ukraine is actively involved in countering Russian propaganda, and significantly increased its participation in joint international and Ukrainian scientific and cultural projects. Particular attention is paid to ensuring the preservation of handwritten documents for the period of their transfer to other institutions for temporary storage during exhibitions and other events and to production of insurance copies of handwritten documents. One of the priority directions of activity of the Institute of Manuscript of the V. I. Vernadskyi National Library of Ukraine today is the digitization of the national manuscript heritage. Conclusions. The full-scale invasion of Russia on the territory of Ukraine turned out to be the biggest challenge for Ukrainian culture during the years of Ukraine’s independence. The protection of objects of the historical and cultural heritage of the Ukrainian people has become one of the strategic directions in resisting enemy expansion. Preservation of the national cultural heritage has become a priority task for the Institute of Manuscript of the V. I. Vernadskyi National Library of Ukraine as a state archival repository either. The Institute of Manuscripts is actively involved in countering Russian propaganda, has significantly increased its participation in joint international and Ukrainian cultural projects, thus contributing to the popularization of the national cultural heritage of Ukraine, the coverage of objective Ukrainian history based on historical documents.

Marinković Veljko, Lazarević Jovana, Marić Dražen

Background: The new circumstances of life due to the proclamation of the COVID 19 virus pandemic have caused numerous changes both in general people's lives and in consumption. Purpose: The aim of this paper is to identify changes in the degree of consumer ethnocentrism when choosing products during the COVID 19 virus pandemic, compared to the period before its occurrence. In addition, differences in consumer preferences for certain domestic products and services before and during the pandemic were analyzed. The paper also deals with differences in ethnocentric tendencies during the pandemic between different socio-demographic consumer segments. Study design/methodology/approach: The primary data were collected from 176 respondents by using the survey method. A paired samples t test is used for hypotheses testing. Independent samples t test and Anova, post hoc Scheffe test, were conducted for analysing differences in ethnocentric tendencies between observed consumer segments during the pandemic. Findings/conclusions: Higher level of consumer ethnocentrism is confirmed in period during the pandemic, especially when it comes to choice of domestic medical products. On the other hand, lower level of consumer ethnocentrism is observed for fashion products and insurance during the pandemic. Older consumers and pensioners exhibit stronger ethnocentric tendencies during the pandemic. Limitations/future research: The main limitation of the paper relates to the use of only a few of the 17 statements within the CET scale for measuring ethnocentric tendencies before and during the pandemic. Also, the research did not cover all categories of domestic products and services. According to the limitations, future studies are recommended to fully apply the CET scale for measuring consumer ethnocentrism. Also, the recommendation is to observe higher number of categories of products and services, and to break down the categories into several subcategories. Finally, future studies can also include some of the determinants of consumer ethnocentrism in the research model.

Sehyun Lim, Taeyeon Oh, Guy Ngayo

This research employs machine learning analysis on extensive data from a prominent Korean life insurance company to substantiate the insurance demand theory, which posits that insurance demand increases with risk aversion. We quantitatively delineate the traits of risk-averse individuals.Our study focuses on a cohort of 94,306 individuals who have filed insurance claims due to illness. To forecast prospective insurance consumers inclined toward additional purchases, we construct a predictive model using a machine learning algorithm. This model incorporates 19 demographic and socioeconomic factors as independent variables, with additional insurance acquisition as the dependent variable. Consequently, we uncover the distinctive characteristics of consumers predicted to acquire supplementary insurance products.Our findings reveal a significant association between the independent variables and the likelihood of purchasing additional insurance. Notably, 10 out of the 19 independent variables exert a substantial influence on additional insurance acquisitions. These characteristics encompass residence in rural areas, a higher likelihood of being female, advanced age, increased assets, a higher likelihood of being blue-collar workers, lower education levels, a greater likelihood of being married or divorced/separated, a history of cancer, and a predisposition for existing policyholders with prior subscriptions to actual loss insurance or substantial insurance contract amounts.Our study holds academic significance by addressing limitations observed in prior research, which predominantly relied on questionnaires to qualitatively assess risk aversion. Instead, we offer specific insights into individual characteristics associated with risk aversion.Moreover, we anticipate that Korean insurance companies can leverage these insights to attract new clientele while retaining existing members through predictive risk aversion analysis. These findings also offer valuable insights across a spectrum of disciplines, including business administration, psychology, education, sociology, and sales/marketing, related to individuals' risk preferences and behaviors.

Long Chen, Mao Ye, Alistair Milne et al.

This report, commissioned by the WTW research network, investigates the use of AI in property risk assessment. It (i) reviews existing work on risk assessment in commercial and industrial properties and automated information extraction from building blueprints; and (ii) presents an exploratory 'proof-of concept-solution' exploring the feasibility of using machine learning for the automated extraction of information from building blueprints to support insurance risk assessment.

Ihsan Chaoubi, Camille Besse, Hélène Cossette et al.

Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.

Stéphane Le Vu, Marion Bertrand, Marie-Joelle Jabagi et al.

There have been reports of myocarditis and pericarditis following mRNA COVID-9 vaccination. Here, the authors use nationwide data from France and find increased risks of these outcomes in the first week following vaccination, for both the first and second dose, and present age- and sex-specific rates.

Yaguan Hu, Yanli Yu

Epidemic disease prevention plays a critical role in ensuring the healthy development of livestock farming, and the subjective willingness of breeders can be affected by the cost of epidemic disease prevention. To correct the misconception that farmers regard the cost of disease control as an ineffective cost, and to promote the healthy development of the pig breeding industry, our study employed the data envelopment analysis super-efficiency model and panel threshold regression model to evaluate the combination of the cost of epidemic disease prevention and swine productivity using data collected from 1998–2018 across 30 provinces in China. The following results were obtained. (1) The cost of epidemic disease prevention generated a non-linear on swine productivity when the swine farming scale was limited; (2) When the number of animals at the beginning of the year was less than 6.0002, swine productivity was impacted negatively; (3) When the number of animals at the beginning of the year ranged between 6.0002 and 12.9994, the impact was insignificant; (4) A strong correlation was observed between the expenses of epidemic disease prevention and animal productivity when the number of animals at the beginning of the year exceeded 12.9994. These results indicate that publicity should be enhanced to elucidate the combination of epidemic disease prevention and swine productivity among breeders. In addition, the government should introduce relevant policies to encourage the development of large-scale pig farming, such as subsidies for the construction of large-scale farms and insurance.

P. Dr. Fadgyas-Freyler

Introduction According to international publications the burden of mental diseases is considered to be significant and rising. Objectives Scope of analysis is to present 1) patient numbers and 2) direct mental health costs from the database of the National Health Insurance Fund Hungary for patients with F00-F99 ICD code between 2015-2019. Methods An Oracle database was created with direct mental care costs for each patient in a given year with a three-digit ICD code and type of care (primary, specialist, prescribing) and handled via sql queries. Data on capacity and performance came from the NHIF and NSO website for 2008-2019 and were handled via Microsoft Excel. Results Mental problems affected 3 million people (more than 30% of the population) in a five year period, though patient numbers are continuously declining. Almost half of the patients only visit a general practitioner and don’t get a prescription. There is also a drop in proportional mental spending which has fallen from 5,03% to 4,02%. This tendency is accordance with international findings. There is a dramatic fall of inpatient cases and a growing number of outpatient interventions, though we see a move from individual therapy sessions to group interventions and a decline in specialist psychotherapy sessions. We can see a shift towards more young patients both in inpatient and outpatient setting. Conclusions The analysis raises the question whether declining patient numbers and shrinking proportional spending are due to smaller provider capacities and unmet need or a mentally healthier population. Disclosure No significant relationships.

Alessandro Fabris, Alan Mishler, Stefano Gottardi et al.

We conduct an audit of pricing algorithms employed by companies in the Italian car insurance industry, primarily by gathering quotes through a popular comparison website. While acknowledging the complexity of the industry, we find evidence of several problematic practices. We show that birthplace and gender have a direct and sizeable impact on the prices quoted to drivers, despite national and international regulations against their use. Birthplace, in particular, is used quite frequently to the disadvantage of foreign-born drivers and drivers born in certain Italian cities. In extreme cases, a driver born in Laos may be charged 1,000 euros more than a driver born in Milan, all else being equal. For a subset of our sample, we collect quotes directly on a company website, where the direct influence of gender and birthplace is confirmed. Finally, we find that drivers with riskier profiles tend to see fewer quotes in the aggregator result pages, substantiating concerns of differential treatment raised in the past by Italian insurance regulators.

Rüdiger Frey, Verena Köck

In recent years a large literature on deep learning based methods for the numerical solution partial differential equations has emerged; results for integro-differential equations on the other hand are scarce. In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. To show the viability of our approach we discuss several case studies from insurance and finance.

Ragnar Levy Gudmundarson, Manuel Guerra, Alexandra Bugalho de Moura

In this work the ruin probability of the Lundberg risk process is used as a criterion for determining the optimal security loading of premia in the presence of price-sensitive demand for insurance. Both single and aggregated claim processes are considered and the independent and the dependent cases are analyzed. For the single-risk case, we show that the optimal loading does not depend on the initial reserve. In the multiple risk case we account for arbitrary dependency structures between different risks and for dependencies between the probabilities of a client acquiring policies for different risks. In this case, the optimal loadings depend on the initial reserve. In all cases the loadings minimizing the ruin probability do not coincide with the loadings maximizing the expected profit.