Abstract
Abstract 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.
Highlights
Artificial intelligence and machine learning application in banking is constantly increasing
Big data analytics is the technique that had a significant positive influence on artificial intelligence (AI), machine learning (ML), and especially on Deep learning (DL), which is very demanding given the amount of data
The contemporary global economic and financial challenges, including those caused by the COVID-19 pandemic have accentuated the need for adequate AI and ML implementation in risk management
Summary
Artificial intelligence and machine learning application in banking is constantly increasing. Banking risk management is one of the finance fields with the strongest development during past decades, but the needs for further development of this area are constantly increasing. This is one of the reasons why artificial intelligence and machine learning is very important for today’s banking risk management. Second research hypothesis is that based on the stated, recommendations and proposals for successful implementation of the artificial intelligence and machine learning in the banking risk management can be formulated so that maximal positive results could be achieved (and negative to be avoided) in the field of banking risk management, business, and economy as well. The book of abstracts from the conference has been published
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