Control Systems and Computers, N4, 2016, Article 8


Upr. sist. maš., 2016, Issue 4 (264), pp. 71-79.

UDC 330.46: 336.7

Kaydan L.I.,International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine

Smart Technology for the Sustainable Development Process Modeling of the Financial Institutions at Risk to Support the Management Decisions

Under unstable conditions where the management of commercial banks conducts its business, there is a need for the efficient dataware to make their management decisions. One of the most important trend of researches is to solve the problems of banks’ sustainable development by means of lowering the risks of bank services based on the modern information technologies, where supportive tools for management decisions are used applying economic and mathematical models intelligent technologies of modeling. The aim of this article is to substantiate an approach to form and develop an intelligent technology, that models the process of the commercial bank’s sustainable functioning under conditions of interference between the risk of the liquidity and the credit risk. The proposed intelligent technology of modeling is based on the program principal approach for the use of the automated modeling, databases and knowledge bases to support the management decisions. The tools for the intelligent modeling processes of the commercial bank’s sustainable functioning are developed. They are based on the knowledge of the semantic network of the both indicators for the credit risks zones of the clients (natural persons) and for the assessment of the client’s financial state to support management decisions under conditions of interference between the risk of liquidity and the credit risk. The intelligent technology processes the banks’ sustainable development under conditions of risk. It is based on the technology of the knowledge representation and correction of the databases taking into account the features of the credit risk zones using the information from their indicators regarding the natural persons in the social and political environment, in the field of employment, income, property, environment, family, community, physical state, and health. This technology helps to solve the problems of provision for the necessary level of the bank’s credit portfolio quality. Also it will help to choose the optimal relationship between the bank’s profitability and liquidity.

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Keywords:  risk of liquidity, credit risk, indicator, risk zone, knowledge base, intellectualization of modeling. 

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