Akademik Veri Yönetim Sistemi
Journal of Soft Computing and Decision Analytics, cilt.3, sa.1, ss.50-71, 2025 (Scopus)
In the competitive environment of the 21st century, making accurate and swift decisions is of great importance for the success of businesses. In the banking sector, which provides financial services and is critical to the economy, decision-making processes are considered a crucial step in enhancing bank performance, operational efficiency, and customer satisfaction. However, traditional methods that generally rely on past experiences and intuition are found to be inadequate for analysing large data sets. In this context, it is considered that the use of artificial intelligence in analysing large data sets will provide significant advantages by increasing the speed and accuracy of decision-making processes. Therefore, the effective use of large data sets, optimization of decision-making processes, and the use of artificial intelligence to increase bank success can be highlighted as important tools. In this study, the factors determining the success of banks operating in the Turkish banking sector and the factors that should be considered in decision-making processes for bank success were examined using machine learning methods. The study, which covers the period from 2012 to 2022 for 24 banks, classified 43 financial ratios into six groups: capital adequacy, profitability, liquidity, asset quality, balance sheet structure, and incomeexpenditure structure. Thus, the effects of factors determining bank success in the context of these main groups were analysed. Additionally, a comprehensive analysis using all 43 financial ratios was conducted to provide a general examination of the factors determining bank success. The study concluded that machine learning methods, with their high accuracy rates, can be effectively used in decision-making, monitoring, and auditing processes.