<![CDATA[Objective: This study investigates the prospects and practical implications of deploying emerging digital technologies — including Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, Blockchain, Cloud Computing, Robotic Process Automation (RPA), and integrated Digital Banking Platforms — in the pursuit of operational cost optimization within commercial banks. The accelerating pace of digital transformation in the financial sector has created both unprecedented opportunities and complex challenges for banking institutions seeking to enhance efficiency and competitiveness. Method: The research employs a systematic literature review of peer-reviewed publications indexed in Scopus, Web of Science, IEEE Xplore, and Elsevier databases (2020–2025), supplemented by a comparative analysis of traditional and digital banking models. Economic analysis methods, including cost-benefit analysis and quantitative performance indicators, are applied to evaluate the financial impact of specific digital technologies. Results: The results demonstrate that the adoption of AI and ML can reduce operational costs by 20–35%, while RPA deployment achieves savings of 25–50% in back-office processes. Cloud computing migration reduces IT infrastructure expenditure by 15–40%, and blockchain-enabled cross-border payment systems can eliminate up to 50% of transaction processing costs. Commercial banks implementing comprehensive digital transformation strategies report cost-to-income ratio improvements from approximately 65–75% to 35–50%. Digital technologies represent a transformative force in banking cost optimization; however, their effective implementation requires substantial upfront investment, robust cybersecurity frameworks, regulatory compliance, and workforce reskilling. Novelty: This study provides a structured analysis and practical recommendations to guide commercial banks in strategically deploying digital solutions for sustained operational efficiency.]]>
