Nowadays, with the expansion of economic businesses and also the dependency of economic activ-ists on the insurance industry to provide the capitals security, there is now a growing need to identi-fy 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, extensive-ly. 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 ef-ficient 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 pro-vide an accurate estimation for risk evaluation and management. Thus, this system can be consid-ered 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 appro-priate decision-making support tool.