Insurance Pricing Research Articles

An Analytical Study of Ship Hull Insurance in the Liquidity and Profitability of the National Insurance Company

The sea is of paramount importance in the life of man and peoples. Since ancient times, it has represented a tool for communication between nations, a link for civilizational meeting between them, and a source of sustenance. Man’s need and interest in exploiting this wealth moves his curiosity and ambitions to think and search for means that contribute and help in expanding the scope of maritime exploitation and consolidating relations between countries more and more. The maritime field was among the important areas touched by industrial and technological development, especially in the field of maritime transport, which led to the emergence of giant ships capable of embarking on sea voyages and thus moving foreign trade in a positive way. The aim of the research is to know the extent to which ship hull insurance is related to the liquidity and profitability of the National Insurance Company, and where the main research problem is manifested in the extent to which ship hull insurance is linked to the liquidity and profitability of the National Insurance Company. Only the research relied on the questionnaire as a tool for all data and information from the sample of 64 and those who have responsibility are (General Director, Department Director, Division Officer, Unit Officer) and the statistical program was used spss 21 in the calculation standard deviation, simple linear regression, testF ) R² (. The conclusions reached by the research are the evaluation of the degree of risk surrounding the ship and the evaluation of the degree of classification in the classification record is based on which the insurance price for the structures of marine vessels is determined, which shows that the degree of risk varies up and down according to the type of ship, origin, shipping lines, and the experience of the crew and the master. The results of the study also showed the existence of a strong correlation between the liquidity variable and profitability in the respondents' response to the research sample in the National Insurance Company, as there is no profitability without the presence of liquidity and good liquidity expresses the company's ability to meet the payment of its obligations. (*) An extracted thesis, which is part of the requirements for obtaining a higher diploma equivalent to a master’s degree. There are also a number of recommendations, including; Adoption of a unified plan for all insurance branches. To measure the liquidity risk in order to ensure that the company is protected from bankruptcy or its exit from the insurance market in the event of a financial crisis again.

Pricing equity-linked life insurance contracts with multiple risk factors by neural networks

This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.

Factor identification for insurance pricing mechanism using data mining and multi criteria decision making

Factor identification for insurance pricing mechanism using data mining and multi criteria decision making

On a Markovian Game Model for Competitive Insurance Pricing

In this paper, we extend the non-cooperative one-period game of Dutang et al. (Journal of Operational Research 231(3):702–711, 2013) to model a non-life insurance market over several periods by considering the repeated (one-period) game. Using Markov chain methodology, we derive general properties of insurer portfolio sizes given a price vector. In the case of a regulated market (identical premium), we are able to obtain convergence measures of long run market shares. We also investigate the consequences of the deviation of one player from this regulated market. Finally, we provide some insights of long-term patterns of the repeated game as well as numerical illustrations of leadership and ruin probabilities.

The impact of farmers’ assessments of risk management strategies on their adoption willingness

Farmers’ assessments of risk management strategies and how the assessments influence their willingness to adopt these strategies are poorly understood. This study conducts a structured survey of 469 farmers to investigate how farmers assess both crop insurance and crop price insurance, the impact of the assessments on their adoption willingness, and in particular, the differences in assessments and adoption willingness between crop insurance and crop price insurance. Empirical results show that farmers’ assessments on crop insurance can be significantly improved through communication and experience. The most effective way to improve farmers’ assessments on crop price insurance is providing adequate information about the insurance products. Furthermore, farmers’ adoption willingness would be enhanced by improved assessment of both crop yield and crop price insurance. This study provides the first empirical evidence that farmers have limited information to assess insurance, which significantly influences their willingness to adopt. This finding reflects that the dissemination of insurance policies, the effectiveness of insurance, and the availability of insurance services significantly enable farmers’ ability to assess risk management strategies, which potentially increases farmers’ willingness to adopt insurance.

Premium for implicit deposit insurance within Russian state banks

Deposit insurance system (DIS) exists for 17 years in Russia. The major deposit market share belongs to state banks. Ordinary depositors may perceive the status of the bank state ownership to reflect additional deposit safety, even in the excess of the DIS limits. Such a situation is called an “implicit deposit insurance” in the literature. By offering a sort of implicit deposit insurance services state banks might underprice the deposits in excess of DIS limits compared to the private banks. We utilize data from the open sources to measure the scale of the implicit deposit insurance pricing in Russian state banks. We have revealed that Russian state banks pay extra premium all other things being equal. More specifically, the premium is larger in the smallest and the largest state banks, than in the medium-sized ones. Thus, we claim that the implicit insurance premium has a U-shaped form for Russian state banks depending on their asset size. However, Russian state banks underprice all deposits all other things being equal. Additionally, we find out that IRB banks in Russia are more prone to set up higher deposit rates when they take on more risks, than non-IRB banks.

Concordance Probability for Insurance Pricing Models

The concordance probability, also called the C-index, is a popular measure to capture the discriminatory ability of a predictive model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. For the frequency model, the need of two different groups is tackled by defining three new types of the concordance probability. Secondly, these adapted definitions deal with the concept of exposure, which is the duration of a policy or insurance contract. Frequency data typically have a large sample size and therefore we present two fast and accurate estimation procedures for big data. Their good performance is illustrated on two real-life datasets. Upon these examples, we also estimate the concordance probability developed for severity models.

DISCRIMINATION-FREE INSURANCE PRICING

AbstractWe consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.

Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

Autocalibration and Tweedie-dominance for insurance pricing with machine learning

Boosting techniques and neural networks are particularly effective machine learning methods for insurance pricing. Often in practice, the sum of fitted values can depart from the observed totals to a large extent. The possible lack of balance when models are trained by minimizing deviance outside the familiar GLM with canonical link setting has been documented in Wüthrich (2019, 2020, 2021). The present paper aims to further study this phenomenon when learning proceeds by minimizing Tweedie deviance. It is shown that minimizing deviance involves a trade-off between the integral of weighted differences of lower partial moments and the bias measured on a specific scale. Hence, there is no guarantee that the sum of fitted values stays close to observed totals if the latter bias term is dominated by the former one entering deviance. Autocalibration is then proposed as a remedy. This new method to correct for bias adds an extra local GLM step to the analysis with the output of the first step as only predictor. Theoretically, it is shown that it implements the autocalibration concept in pure premium calculation and ensures that balance also holds on a local scale, not only at portfolio level as with existing bias-correction techniques.

Analysis of the Effect of Temperature and Rainfall on Coffee Productivity in Indonesia using the Cobb-Douglas model for Determining Insurance Premiums

Coffee is one of Indonesia's foreign exchange earners and plays an important role in the development of the plantation industry. Indonesia is a coffee bean producing country ranked 4th in the world after Brazil, Vietnam, and Colombia. The agricultural sector in Indonesia has risks and uncertainties including a decrease in production yields which results result in a decrease in farmers income. The risk of loss in coffee is caused by temperature and rainfall. Efforts that can be made to reduce losses are through risk transfer through agricultural insurance. The purpose of this study to analyze the effect of temperature and rainfall on coffee productivity in Indonesia and determine the insurance premium. This research uses data on coffee productivity, temperature, and rainfall from 1980-2019. The relationship between coffee productivity as a dependent variable while temperature and rainfall as an independent variable was used the Cobb-Douglas method. The results that will be obtained from this study indicate the temperature and rainfall affect coffee productivity in Indonesia, and obtain insurance issued by the farmers to the insurance companies. The results obtained from the data analysis show that temperature and rainfall have an effect on coffee productivity in Indonesia. The results of productivity predictions are used as the basis for determining the price of insurance premiums issued bye insurance companies.

Derivatives or climate insurance? Risk management tools for a soybean farmer in Brazil

This paper aims to analyze whether there is a difference between the cost/benefit of climate risk reduction and cost/benefit of soybean price in Palotina, Paraná, Brazil. The data were collected from bibliography, official and private documents. Climate data were analyzed based on the releases content. The methodology to analyze de future market data was the same as the B3. Options were analyzed as Black & Scholes model. Also, this paper developed the Leismann & Bortoluzzi index to analyze the protections cost/benefit. To compare mitigation costs, there were used the t student test. Limitations were about the Black & Scholes model, which does not consider subjective variables. Cost/benefit index of price protections were compared with the climate insurance index in order to test if there was a statistical difference between them. All tests allowed to infer that the indices are statistically different. This study concluded that climate and price insurance are excellent tools for rural enterprise risk management, and there was significant evidence to infer that the protections are feasible. Or rather, that the farmer is exposed to both types of risk and that the forms of mitigation are satisfactory in both cases.

Country factor behavior for integration improvement of European life insurance markets

This paper provides evidence of the role that financial market development and institutional quality play in the integration of European Union (EU) life insurance markets. It analyzes ten EU life insurance markets over a seventeen-year sample period. The meta-technology cost/revenue efficiency ratios, estimated under the meta-frontier DEA framework, are used as a measure of integration and the analysis is conducted by applying Tobit panel regression models. We find that national stock market development and institutional quality enhance cost performance and integration of EU life insurance markets. Results also show that in countries where bancassurance is the main life insurance distribution channel, banking sector development contributes to integration in terms of revenue efficiency. However, we find that better outcomes in national institutional quality decrease the meta-technology revenue efficiency ratio, suggesting that life insurance prices are lower in countries with better institutions.

A comprehensive model for cyber risk based on marked point processes and its application to insurance

After scrutinizing technical, legal, financial, and actuarial aspects of cyber risk, a new approach for modelling cyber risk using marked point processes is proposed. Key covariates, required to model frequency and severity of cyber claims, are identified. The presented framework explicitly takes into account incidents from malicious untargeted and targeted attacks as well as accidents and failures. The resulting model is able to include the dynamic nature of cyber risk, while capturing accumulation risk in a realistic way. The model is studied with respect to its statistical properties and applied to the pricing of cyber insurance and risk measurement. The results are illustrated in a simulation study.

Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities

Abstract The socio-economic development of municipalities is defined by a set of indicators in a period of interest and can be analyzed as a multivariate time series. It is important to know which municipalities have similar socio-economic development trends when recommendations for policy makers are provided or datasets for real estate and insurance price evaluations are expanded. Usually, key indicators are derived from expert experience, however this publication implements a statistical approach to identify key trends. Unsupervised machine learning was performed by employing K-means clusterization and principal component analysis for a dataset of multivariate time series. After 100 runs, the result with minimal summing error was analyzed as the final clusterization. The dataset represented various socio-economic indicators in municipalities of Lithuania in the period from 2006 to 2018. The significant differences were noticed for the indicators of municipalities in the cluster which contained the 4 largest cities of Lithuania, and another one containing 3 districts of the 3 largest cities. A robust approach is proposed in this article, when identifying socio-economic differences between regions where real estate is allocated. For example, the evaluated distance matrix can be used for adjustment coefficients when applying the comparative method for real estate valuation.

تسعير تأمين الودائع بالبنوک الإسلامية المصرية Deposit insurance pricing in Egyptian Islamic banks

المستخلص يهدف هذا البحث إلي تقدير سعر- تکلفة -التأمين على الودائع بالبنوک الإسلامية العاملة في مصر باستخدام البيانات بالقوائم المالية لـ 13 بنک فى 2018- 2019 باستخدام نموذج Merton ونموذج تسعير الخيارات. وتشيرالنتائج إلى وجود اختلاف کبير فى أقساط التأمين بين البنوک من أعلى معدل2.5% فى المصرف المتحد إلى أدنى معدل 0.0001 % فى بنک الشرکة المصرفية العربية الدولية. ويشيرتحليل الإنحدار إلى أن هناک دلالة إحصائية إيجابية (عند مستوى المعنوية 5%) بين أقساط التأمين وخطرالإئتمان. وهذه النتائج تبين ملاءمة تطبيق سياسة التأمين على أساس مخاطر البنک بدلا من سياسة السعر الثابت بالبنوک الإسلامية. ويستفاد من نتائج هذا البحث فى تخفيض الأخطار السلوکية، ويوصى البحث بتطبيق قسط التأمين المبنى على أساس المخاطر بدلا من القسط الثابت بالبنوک الإسلامية، وأيضا الاسترشاد بما توصل إليه هذا البحث. Abstract This research aims to estimate the price-cost-of deposit insurance in Islamic banks operating in Egypt using data in the financial statements of 13 banks in 2018-2019 using the Merton model and the options pricing model. The results indicate that there is a big difference in insurance premiums between banks, from the highest rate of 2.5% in The United Bank to the lowest rate of 0.0001% in the International Arab Banking Company. The regression analysis indicates that there is a statistical significance (the level of significance 5%) positive between insurance premiums and credit risk.. These results demonstrate the suitability of applying risk-based insurance premium policy in Islamic banks rather than flat rate policy in Islamic banks. The results of this research are used to reduce moral hazard behavior. The research recommends applying risk-based insurance premium instead of the flat rate,, in Islamic banks, as well as being guided by the findings of this research

Ecological footprints jeopardy for mineral resource extraction: Efficient use of energy, financial development and insurance services to conserve natural resources

The commodity and resource markets played an important role in attaining the country's economic development via energy efficiency, economic insurance prices and sound financial development. However, the enormous pressure of human demand on natural capital exhausts many important rare earth's resources that demolished the global resource conservation agenda. This study examines the stated factors in the top 10 mineral abundant economies by using their latest available data series from 1990 to 2019. The results of the study show that ecological footprints on mineral resources confirmed the hump-shaped relationship between them. The enormous human demand for natural capital is viewed further in the environmental accounting system. Ecological footprints cause a greater extraction of mineral resources that jeopardize future stocks of rare earth's resources. The panel estimates show that insurance and financial services, massive population growth, and continued economic growth decreases mineral resource rents. In contrast, energy efficiency and trade policies increase mineral resource rents across countries. In general, the results support the economic policies that help reduce human demand for natural capital by efficient insurance services, sustainable economic growth, energy efficiency, and trade fair policies. These factors required more sustainable efforts to mitigate adverse environmental externalities by adopting the latest technology in extractive industries to prevent unsolicited mineral losses.

TripMD: Driving patterns investigation via motif analysis

Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. Additionally, we test our system using the UAH-DriveSet dataset, a publicly available naturalistic driving dataset. We show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.

Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when complicated models are used. Furthermore, from both rate-making and rate-regulation perspectives, it is critical to investigate the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors. ANN models are applied to meet this goal, and variable importance is measured to improve the model’s explainability due to the models’ complex nature. The results obtained from different variable importance measurements are compared, and dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for applications in auto insurance rate regulation. This study focuses on analyzing major risks only, but the proposed method can be applied to more general insurance pricing problems when additional risk factors are being considered. In addition, the proposed methodology is useful for other business applications where statistical machine learning techniques are used.

Making Confident Decisions with Model Ensembles

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information into the approach, appropriate to different collections of models.