This dissertation thesis deals with problems of identifying client’s solvency in the insurance business and is drawn for the insurance companies needs. The main target of this work is a construction of methodology, which will provide managers a tool to support their decision making in cases of client solvency assessment. The basic theoretical background, an overview of the current state of the analyzed subject and the description of utilized methods are presented in the introductory part of this work. In following parts of this work is introduced a real database of insurance company’s clients, which serves as a basis to accomplish the defined goal. The source data were subject to a necessary analysis to determine the cross-correlations and variables entering the decision-making model. A large variety of traditional statistical methods, including relevant software were used to analyze the data. Decision-making model was formed with the help of artificial intelligence methods, especially fuzzy logic. The technical realization of the model was made using MATLAB software. The process of insurance company’s client solvency assessment methodology creation is described in detail and elaborated into phases. The fundamental part of the methodology, decision-making model, can be easily modified and adapted to the end user’s specific needs. The text also includes a verification and implementation of the model, an interpretation of the results, a comprehensive client solvency assessment methodology process in insurance business and the definition of contribution of this methodology to practice, theory and pedagogy
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