This study is devoted to the problems of improving the banking risk management, taking into account the new regulatory and technological requirements based on the use of modern technology and combining the latest achievements in artificial intelligence, numerical mathematics, statistics and information technology.The paper analyzes the characteristics of banking risks, the main methods of assessment used in practice. The authors propose new prospective approaches to assessment, based on the most modern methods of data analysis,identify prospective directions for banking information system improvement and suggest the possibility of their implementation.The example of Ukrainian banks shows the main problems of using new approaches to risk assessment and its information support. The article proposes the ways to overcome them.Key words: banking risk management, artificial intelligence, banking risks estimation, data analysis

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Application of predictive models on the basis of data mining confirmed its expediency in solving many economic problems. One of the crucial issues is the assessment of the borrower's creditworthiness on the basis of credit scoring models. This paper proposed an ensemble-based technique combining selected base classification models with business-specific feature selection add-on to increase the classification accuracy of real-life case of credit scoring. As the model limitations have been used easy-understandable algorithms on open-source software (R programming). The statistical results proved that hybrid approach for user-defined variables can be more than useful for ensemble binary classification model. It is shown that a great improvement can be reached by applying hybrid approach to feature selection process on additional variables (more descriptive ones that were built on initial features) for this real-life case with limited computational resources.

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