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We have developed a model for a life-insurance policy. In this model, the net gain is calculated by computer simulation for a particular type of lifetime distribution function. We observed that the net gain becomes maximum for a particular value of upper age for last premium.

We present an analytical study of an insurance company. We model the company's performance on a statistical basis and evaluate the predicted annual income of the company in terms of insurance parameters namely the premium, the total number of insured, average loss claims etc. We restrict ourselves to a single insurance class the so-called automobile insurance. We show the existence of a crossover premium p_c below which the company is operating at a loss. Above p_c, we also give a detailed statistical analysis of the company's financial status and obtain the predicted profit along with the corresponding risk as well as ruin probability in terms of premium. Furthermore we obtain the optimal premium p_opt which maximizes the company's profit.

Using the China Household Finance Survey data in 2011, the estimation results of structural equation modeling demonstrate that the respondents with higher time preference rate have a significant higher probability of investing in stocks, which implies that the short-term households will prefer stock investment. The social insurance programs and insurance policies held by the family will have a significantly direct positive effect in promoting stock investment and also a significantly direct positive effect on the respondent’s time preference, which could further indirectly increase the family’s stock investment. These results show that the safety-net built by the Chinese government, including the social security and commercial insurance, is very likely to attract more short-term investors into the stock market. These empirical results provide new evidences to explain the extreme volatility of Chinese stock market and also testify the policy effect of building an environment for people to possess property income in China.

This paper reviews empirical studies with a particular interest in Islamic finance literature and highlights future research directions. The earlier literature on Islamic finance was built on the Islamic economic foundation of social justice and fairness, which was formed theoretically from the primary sources of Sharia coupled with some analytical frameworks. Subsequent studies emphasized the empirical investigations without including far-reaching analytical and theoretical postulations in the area. Although empirical studies on Islamic banking are plenty, there is a new body of emerging empirical literature on Islamic finance focusing on corporate finance and Takaful, whereas Islamic accounting studies are mostly qualitative. The literature provides a mixed picture of Islamic financial markets and instruments, showing that the Islamic ones perform better most of the time but also perform worse at times than their conventional counterparts. This paper discusses issues that are relevant to Islamic finance and identifies avenues for future research and policy implications.

Natural disasters — earthquakes, hurricanes and other storms — cause substantial property damage and loss of life in many parts of the world. The relative infrequency and importance of extreme cases leads to a preferential use of simulation models over historical statistical/actuarial models in studying the impact of such catastrophes on insurance systems. Given the increasing awareness of the highly intermittent nature of geophysical phenomena, modelers need to revisit their assumptions not only of the geophysical fields, but also of the geographical distribution of insured property as well. This paper explores the distribution of insured property through the lens of multifractal theory.

The terms reliability, warranty and insurance are interwoven in some ways. The warranty of system and insurance coverage offered by the manufacturer or third party reflects its potential reliability. In certain situations, the insurance may expire before the warranty time. To address this issue, the reliability and sensitivity analysis of a single-unit insured system has been carried out. The system may functions in warranty period with or without insurance cover whereas no insurance cover is given during nonwarranty period. If faults are covered under warranty or the insurance terms, the manufacturer or insurance company is liable to pay all the repair/replacement costs; otherwise, the user must pay the entire cost. The reliability metrics of the system and factors impacting system profitability are derived using Markov and regenerative processes. Profit equations and sensitivity functions are established for all stakeholders. The sensitivity function for system availability is also derived for all the possible considered periods. Exponential distribution is used to illustrate the developed model numerically.

A quadratic discrete time probabilistic model, for optimal portfolio selection, under risk constraint, is introduced in the context of (re-) insurance and finance. The portfolio is composed of contracts with arbitrary underwriting and maturity times. For positive values of underwriting levels, the expected value of the accumulated final result is optimized under constraints on its variance and on annual Returns On Equity. Existence of a unique solution is proved and a Lagrangian formalism is given. An effective method for solving the Euler-Lagrange equations is developed. The approximate determination of the multipliers is discussed. This basic model, which can include both assets and liabilities, is an important building block for more general models, with constraints also on non-solvency probabilities, market-shares, short-fall distributions and Values at Risk.

In this paper, we study the valuation of variable annuities for an insurer. We concentrate on two types of these contracts, namely guaranteed minimum death benefits and guaranteed minimum living benefits that allow the insured to withdraw money from the associated account. Here, the price of variable annuities corresponds to a fee, fixed at the beginning of the contract, that is continuously taken from the associated account. We use a utility indifference approach to determine the indifference fee rate. We focus on the worst case for the insurer, assuming that the insured makes the withdrawals that minimize the expected utility of the insurer. To compute this indifference fee rate, we link the utility maximization in the worst case for the insurer to a sequence of maximization and minimization problems that can be computed recursively. This allows to provide an optimal investment strategy for the insurer when the insured follows the worst withdrawal strategy and to compute the indifference fee. We finally explain how to approximate these quantities via the previous results and give numerical illustrations of parameter sensitivity.

Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. To our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a random star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.

The traditional financial industry has changed drastically with innovations in digital technology in the sector of finance. The new technology has not only altered the operations of financial services, but also changes the value chain of financial sector. Blockchain is one such technology which has proved to be a game changer in the financial industry. There are various studies on application of blockchain technology in the financial sector. This paper recommends a blockchain-based framework for the insurance industry. The need for this study is that there is an increasing requirement to improve the efficiency and customer experience, reduce the chances of fraud in insurance industry. Blockchain technology can prove to be a solution to the above-mentioned challenges. The methodology used to conduct this study is secondary data analysis and vast literature review. This study finds that there are various studies conducted in identifying the scope and application of blockchain in insurance industry but none of them suggests any framework to be implemented. This study suggests a framework to implement blockchain technology in insurance industry based on industry and academic literature.

This paper describes the research methods, results and implications to date of an ongoing series of studies on damage, damage costs and damage ratios for various types of New Zealand property, i.e. houses and their contents, low-rise non-domestic buildings of various ages, and plant equipment and stock in various non-domestic situations. The statistical properties of the distributions of damage ratio have been evaluated as a function of Modified Mercalli (MM) intensity, up to MM10. Using the damage ratios, the relative vulnerability of different classes of buildings, equipment and stock have been evaluated. All subsets of the data (from two earthquakes of M_w6.6 and M_w7.8 respectively) were found to have damage ratios fitting the truncated lognormal distribution well. The mean damage ratios were, in general, much less than previously believed. In a microzoning study of Napier, which was close to the fault rupture of the M_w7.8 1931 Hawke's Bay earthquake, it was found that single-storey houses were less damaged on soft ground (harbour reclamation) than on stiffer ground. The application of damage ratios to property in site-specific or macro-scale scenarios will provide models of future earthquake damage outcomes. Such models may enable greatly improved planning decisions to be made for land-use, risk management, insurance, emergency responses and national or regional economic provisions.

Motivation to fit the Danish reinsurance claim and aircraft windshield data sets, we introduce exponentiated half logistic-power series (EHLPS) distributions, which are obtained by compounding exponentiated half-logistic distribution with power series distributions. Some mathematical properties have been studied. Maximum likelihood estimation of unknown parameters for the complete data is discussed via the EM algorithm. Simulation results to assess the performance of the estimation methods are discussed. Finally, two applied examples are given for indicating the flexibility and appropriateness of the distribution.

Climate change impacts are stochastic and highly uncertain and moreover heterogeneous across regions. That is, there is a potential to sharing this risk ex-ante across regions and hence to reduce the welfare-economic costs of these risks. We analyze how climate risks could be reduced via an insurance scheme at the global scale across regions and quantify the potential welfare gains. We introduce risk sharing of global climate risk represented by the equilibrium climate sensitivity, and introduce an asset-based insurance scheme to allow for risk sharing across regions. We estimate that such risk sharing scheme could lead to welfare gains reducing the global costs of climate change by up to 15%. Such a scheme implies transfers of about USD 200 billion per year and faces important implementation challenges. The results provide a motivation for the loss and damage mechanism related negotiations about sharing risks of climate change at the global level.

I present a model of the health capital investment decision of a firm using a moral hazard framework. Health capital investment increases the probability that a worker is present and productive. The firm cannot verify a worker's health capital investment decision. When a firm invests in health capital, the investment is verifiable because the firm contracts with the insurer. I derive the optimal contract for when the worker and for when the firm invests in health capital. When the firm invests in health capital, the level of investment is higher and wages are less volatile. In my model, firms invest more than workers because of a production externality and because it is less costly to invest in health capital than to compensate the worker for bearing the risk of an uncertain labor realization. This result improves welfare, contrary to the benchmark that workers consume more health care than is efficient ex post when firms provide health insurance. Unlike the benchmark model of a worker and insurer, my model includes a profit maximizing firm, includes an endogenous probability of getting sick, and allows the insurer to set premiums by anticipating the health care investment level of the insured.

Personal Finance Management (PFM) is crucial to control money to ensure that people have a comfortable present as well as secure future. It’s important for every individual to manage his finance digitally in the digital age. This study aims to identify the main dimensions of PFM from literature review and empirically test the impact of its each domain on the overall Digitalized Financial Management Behavior (DFMB). Also, the study verified the psychometric properties of the tool used to measure Personal Financial Management Behavior (PFMB) using digital platforms. The questionnaire was online administrated using Google forms among 388 young adults in the National Capital Territory (NCT) of India. The Kaiser–Meyer–Olkin (KMO) test of sampling adequacy and Bartlett’s test, linearity assessment, data screening were done in International Business Machines Statistical Package for Social Sciences (IBM SPSS). Factor analysis was done for unidimensionality assessment, bootstrapping (5000 resamples), and reliability and validity statistics in SmartPLS. The study identified six key dimensions of PFM from the existing significant literature and empirically verified the psychometric properties of the instrument in digital context. The resultant instrument includes 25 items under the six constructs. The study found that spending, credit management, saving behavior, personal cash management, investment and insurance are theoretically and empirically important determinants of PFM practices using digital platform therefore more attention need to be paid to these determinants for promoting sound DFMB.

Organizations use many tools to perform risk analysis. Failure Mode and Effects Analysis (FMEA) is one such tool which ranks highly as a modern and widely used tool. Despite its advantages, FMEA experiences limitations within the insurance industry. The main aim of this paper is to enhance the capability of using FMEA in the insurance industry. As with any process, FMEA is prone to failure if its weaknesses are not recognized and supplemented with other activities. This paper seeks to overcome the issues by obtaining the probability distribution of the Risk Priority Number which is an indicator of the overall risk in FMEA. Finally, an empirical study in the area of gas industry is used to demonstrate the capability of the new idea.

In this paper, we shall propose a Monte Carlo simulation technique applied to a G2++ model: even when the number of simulated paths is small, our technique allows to find a precise simulated deflator. In particular, we shall study the transition law of the discrete random variable :

Deep learning is a type of machine learning known for its competitive advantage in discovering complex relationships in all data types. However, the insurance applications of deep learning were used for damage detection and churn prediction applications, while the premium prediction received low attention from previous researchers. This study aims to build an incremental deep learning model to predict insurance premiums. The model contributes to the previously studied Usage-Based Insurance (UBI) concept. We propose a deep learning model consistent with the UBI concept that considers the available factors affecting the premium to predict the insurance premium. The proposed model consists of two parts. Part one is the Convolutional Neural Network (CNN) for deep features extraction. Part two is the Support Vector Regression (SVR) built on the extracted deep features to predict the premium. The proposed model is called CNN-SVR after combining the two parts of CNN and SVR. The dataset was collected from an insurance company to train the proposed model and evaluate its performance compared to the other classical models adopted previously by other researchers, namely the Neural Network (NN), Random Forests (RF), Decision Trees (DT), Linear Regression and Support Vector Regression (SVR). The model performance evaluation was achieved using some metrics and the execution time needed to add a new data point to the model. The selected metrics are the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage of Error (MAPE), Explained Variance (EV), Correlation Coefficient (R), and t-test. The proposed CNN-SVR model reported the best averages among the other models of 1363.935 MSE, 36.838 RMSE, 18.774 MAE, 11.940 MAPE, 0.957 R, and 1 − p values close to 1 in the t-test. The proposed incremental model reported a faster execution time than the classical models, which need to be retrained fully to add a new data point. The study concluded that CNN-SVR model outperforms the other models in prediction performance and execution time, which supports the hypothesis. A possible future direction for this study is to use a larger dataset with more factors affecting the premium for a better contribution to the UBI and predictions.

In the past decade, the insurance business has experienced remarkable growth, as evidenced by the introduction of numerous novel products such as chatbots, digital claims, telemetric insurance and robo-advisors. The insurance industry is also experiencing a significant transformation resulting from the digital transformation. The use of digital technology opens the door for inventive architectures and the development of innovative products in the future that will benefit both insurance companies and their clients. By integrating Internet of Things devices, digital innovation revolutionizes how insurance companies collaborate with industries, ultimately benefiting all parties. This chapter reviews the history of digital innovation in insurance, also known as insurance technology (InsurTech), as well as how technology has underpinned the expansion of InsurTech in recent years. The chapter also examines previous and current studies on the use of technology in insurance transactions and how the implementation of digitalization affects the industry’s long-term viability. The research identified three key InsurTech concepts that have revolutionized the insurance sector. New entrants could disrupt the entire insurance distribution process and introduce new consumer value propositions to attract and retain clients, challenging current business models. InsurTech improves incumbent insurers’ value chain by providing innovative technologies and solutions that boost efficiency and reduce costs.

Insurance companies have to build a reserve for their future payments which is usually done by deterministic methods giving only a point estimate. In this paper two semi-stochastic methods are presented along with a more sophisticated hierarchical Bayesian model containing MCMC technique. These models allow us to determine quantiles and confidence intervals of the reserve which can be more reliable as just a point estimate. A sort of cross-validation technique is also used to test the models.