Insurance

Insurance companies are among the service organizations, which maintain close relationships with their clients by providing insurance services. Clients are the most important resource for service companies. And profitability of insurance companies undoubtedly hinges on clear analysis of client satisfaction and improved productivity of service providers. An important factor of client satisfaction with insurance services in insurance companies is short policy issuance lead time. Insurance companies have rarely paid enough attention to this problem; therefore, this paper aims to provide a strategy for improving policy issuance lead time by simulating the existing system and observing simulation results. After a software model was developed, the discrete-event simulation (DES) results were analyzed. Then model validation tests were conducted. After a valid simulated model was developed, it was decided to design system improvement scenarios. According to the results, policy issuance lead time decreased by changing each of these parameters: the number of operators issuing insurance policies, client referral time to a specialist, specialized test response time, and insurance representative referral time to a policy issuance center. As a result, the service process improved, and productivity increased. This indicates the effectiveness of the DES technique and expansion of operations research methods in the service industry.

One of the innovations that has been formed in the insurance industry in recent years is transfer the risk to the capital markets. Today, this possibility is provided by issuing insurance bonds and Catastrophe bonds, which are a most important type of insurance-linked securities (ILS), can redress inefficiency in the insurance industry.Today, more and more Catastrophe (CAT) bonds are being issued worldwide, which is welcomed by investors and insurance companies. On the other hand, traditional insurance solutions to cover the risks of Iran's oil and gas industry is not efficient and sufficient and using CAT bonds to transfer risks of this industry to capital markets is a necessary and inevitable issue.The aim of this research is to identify effective factors for issuing Catastrophe bonds in Iran's oil and gas industry. On this basis and after reviewing the literature through library studies, 33 factors were identified in the form of seven categories, based on the similarities. Then, based on Delphi method, experts were asked to express their opinions through an iterative questionnaire. After take the experts' opinions in every round, the statistics analysis was performed and the Delphi process was stopped in the third round. Based on the results, the number of 32 factors in six categories with the titles Legislation and Amendment of the Rules, Knowledge Management, Process Management, Transparency, Creation and Strengthening of Software Platforms and Cultivation were approved by experts and identified as effective factors for issuance of Cat bonds in Iran's oil and gas industry.

Stakeholders of an organization avoid undesirable outcomes caused by ignoring the risks. Various models and tools can be used to predict future outcomes, aiming to avoid the undesirable ones. Early warning models are one of the approaches that could help them in doing so. This study focuses on developing an early warning system using machine learning algorithms for predicting solvency in the insurance industry. This study analyses 23 financial ratios from Iranian general insurance companies listed on the Tehran Stock Exchange between 2015 and 2020. The model uses Decision Tree, Random Forest, Artificial Neural Networks, Gradient Boosting Machine and XGBoost algorithms, with Boruta as a feature selection method. The dependent variable is the solvency margin ratio, and the other 22 ratios are the independent variables, which Boruta reduces to 7 variables. Firstly, the performance of the machine learning models on two datasets, one with 22 independent variables and one with 7, is compared based on RMSE values. The XGBoost algorithm performs the best on both data sets. Additionally, the study predicts the 2020 values for 19 insurance companies, performs stage classifications, and compares actual stages to predicted stages. In this analysis, Random Forest has the best estimate accuracy on both data sets, while Gradient Boosting Machine has the best estimate accuracy on the Boruta data set. Finally, the study compares the machine learning models' results in terms of capital adequacy classification, where Random Forest performs the best on both data sets, and Gradient Boosting Machine on the Boruta data set.