APPLICATION OF MACHINE LEARNING METHODS IN THE BANKING SECTOR TO INCREASE THE EFFICIENCY OF DECISION-MAKING

This article examines the modern banking sector in the context of its transformation through the integration of machine learning (ML). This process has become crucial for identifying new avenues for the optimization and automation of financial transactions. In the initial research phase, a deep analysis of existing decision-making methodologies in banks was conducted, focusing on their role in current conditions and the potential to replace human involvement with algorithms. The evaluation and calculation of ML model efficiency revealed certain challenges, such as overfitting and a lack of transparency, but also highlighted significant advantages over traditional systems. When selecting tools for the implementation and deployment of ML, a need for specialized tools tailored for the banking sector was identified, considering the specific nuances of this market. Defined efficiency-enhancing directions include developing new regularization methods, improving cross-validation techniques, and advancing explainable AI. The study also emphasized the ethical concerns arising from ML implementation. Notably, primary attention was given to ensuring user data confidentiality and reducing algorithmic bias. Based on the research results, a conclusion about the immense potential of machine learning in the banking sector was drawn. While certain challenges exist, the right approach can lead to the creation of more efficient, secure, and transparent systems, enhancing client trust.