Business risk evaluation and management of Iranian commercial insurance companies

Abstract

Nowadays, with the expansion of economic businesses and also the dependency of economic activists on the insurance industry to provide the capitals security, there is now a growing need to identify and evaluate risks of the insurance industry. Therefore, in this study, a comprehensive model was developed to evaluate and manage business risk by reviewing the research literature, extensively. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS) was developed for every business risk after identifying the relevant dimensions and the criteria and collecting the necessary data from the central insurance databases. Finally, a general model was presented to evaluate and manage risks of the insurance industry. Four major problems were also considered: optimal and efficient normalization, optimal training for testing ratio for every neural network, model validation, and the easiness of user communication with the system. The results show that the model can provide an accurate estimation for risk evaluation and management. Thus, this system can be considered as an appropriate tool for business risk evaluation and management of insurance companies. Furthermore, the effectiveness of this method in evaluating and managing the risk at insurance companies can be turned into a neural network and such a neural network can be used as an appropriate decision-making support tool.

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

“…Risk calculation and prediction were reported in the literature based on statistical techniques (Micán et al, 2022;Ponsard et al, 2019) and Artificial Intelligence (AI) models (Neumeier et al, 2018;Zhang, 2020). Moreover, neuro-fuzzy models, hybridising ANN and FIS, were also proposed to handle different problems such as the insurance business risk estimation (Hessami, 2018), the overseas construction projects decision prediction (Utama et al, 2019), and stock market segment shocks detection and prediction (Yousofi Tezerjan et al, 2021). However, the latter models were not used to compute the OPR.…”

An integrated framework (CTSR-BWG) for outsourcing decisions in a marine manufacturing firm

“…(Bertolini, et al, 2004;Nayak, et al, 2007;Al-Kahtani, 2018). Understanding opportunities and challenges, and in other words, identifying and analyzing outsourcing risks (Hessami, 2018) and determining the effective criteria for outsourcing decision making are important, and lack of understanding can damage the organizations; In outsourcing literature, there are various risks such as disclosing key information and strategic directions, disclosing research and development projects and new ideas, elimination of competitive advantage, etc. (Motadel, et al, 2011).…”