THEORETICAL СONСEPTUАLІZАTІON OF BАNKІNG RІSK MАNАGEMENT

The article explore the theoretical aspects of banking risk management, its purpose, objects, subjects and advantages are singled out. Effective banking risk management should be considered as the main task of banking institutions in their development. From this research of theoretical foundations of banking risk management, we consider it а new direction of scientific research in Ukraine, so the theoretical and methodological developments of these problems are relevant today. In order to solve these problems, we propose to distinguish between «management of banking risks» and «banking risk management». In the research of the theoretical foundations of banking risk management, the interpretation of these basic concepts was proposed. Consequently, the authors proposed defined «management of banking risks» as a process that includes methods and techniques for identifying, assessing, monitoring, controlling and forecasting bank risks, in order to achieve the main objectives of the banks. And «banking risk management» defined as а set of principles, means and forms of management of the bank’s activities related to risks. For the development of the theory of banking risk management the authors proposed identified and characterized his subjects and objects. It is determined that the object is risks of banks and economic relations at risk, and the subject is the employees of the structural units, which, through the application of knowledge, skills, information and financial resources, participate in the management of banking risks. In addition, the advantages of effective risk management in the activities of banks are determined. The lack of research on the theoretical aspects of banking risk management can lead to deepening of theoretical and methodological problems and may negatively affect in the practical activities of banks. Keywords: management of banking risks, banking risk management, bank, subjects and objects of banking risk management. JEL Classification G21, G28 Formulas: 0; fig.: 1; tabl.: 0; bibl.: 12.

Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management

Artificial intelligence and machine learning have increasing influence on the financial sector, but also on economy as a whole. The impact of artificial intelligence and machine learning on banking risk management has become particularly interesting after the global financial crisis. The research focus is on artificial intelligence and machine learning potential for further banking risk management improvement. The paper seeks to explore the possibility for successful implementation yet taking into account challenges and problems which might occur as well as potential solutions. Artificial intelligence and machine learning have potential to support the mitigation measures for the contemporary global economic and financial challenges, including those caused by the COVID-19 crisis. The main focus in this paper is on credit risk management, but also on analysing artificial intelligence and machine learning application in other risk management areas. It is concluded that a measured and well-prepared further application of artificial intelligence, machine learning, deep learning and big data analytics can have further positive impact, especially on the following risk management areas: credit, market, liquidity, operational risk, and other related areas.

The Essence of Compliance and its Place in Banking Risk Management

Annotation. Introduction. The versatility of modern international banking, instability of the global financial environment and dynamism of normative regulation necessitates a strong need not only to implement standards and regulations of banking, but also to follow such guidelines and monitor compliance with their principles and norms. In such conditions, the study of the essence, organizational and economic principles and features of the practical implementation of compliance in banks is a particularly important scientific task. Purpose. The aim of the paper is to study the scientific and theoretical foundations, analysis of the compliance system in conditions of internationalization of the banking business. Results. It has been determined a historical prerequisites of “compliance” and it has been offered the own definition of compliance which means a system of the actions carried out for the purpose of observance of internal rules and external requirements at the same time as a whole by the organization, its separate divisions and employees. It has been confirmed the essential value of the qualitative classification of banking risks, which is used as a result to find reserves to improve the efficiency of risk management of banking operations. The compliance system has been presented in terms of elements that affect the implementation of compliance risk. Compliance risk as a non-financial banking risk has been studied and determined that it can be both a source and a consequence of the realization of other types of risk. It has been analyzed the structure of compliance costs of banks and financial institutions of European Union in 2019. Conclusions. In order to increase effective compliance control in the international banking business, artificial intelligence technologies are used. It has been concluded that in the future banking institutions should focus on developing a strategy that will at the same time reduce costs and maintain a reliable system of compliance

Research on Risk Measurement and Early Warning of Electronic Banking Business Based on GMDH Algorithm

In order to deal with the problems of traditional e-banking risk measurement and early warning methods, such as low accuracy of e-banking risk measurement and longer early warning time, an e-banking risk measurement and early warning method based on the GMDH algorithm is proposed. This scheme mines the e-banking risk measurement and early warning indicators by the GMDH algorithm, and it will input the influencing factors and risk factors as independent variables into the GMDH modeling network and then input the e-banking business growth rate as the dependent variable into the GMDH modeling network which is standardized by the normative method of processing the e-banking business risk measurement and early warning index data. According to the processing results, it calculates the weight of the measurement and early warning index by the entropy method, and it constructs the e-banking risk measurement model with the genetic algorithm which can help to calculate the optimal solution of the parameters, formulate the risk measurement interval, and determine the risk in order to realize the risk warning of electronic banking business. The simulation results show that the proposed method has a higher accuracy of e-banking risk measurement and a shorter warning time.

DIRECTIONS FOR IMPROVEMENT OF BANKING RISK MANAGEMENT

Banking risks are one of the most important problems of credit institutions, as they have a direct impact on the financial situation of a commercial bank and the entire financial system of our country. This article discusses the essence of banking risk, its types and causes. The reasons for the occurrence of bank risks of both external and internal nature are summarized. The principles of creating an effective risk management system are systematized. Strategic and tactical methods and tools of risk management are summarized. The mandatory elements of improving the efficiency of the bank risk management system are identified.