ARTIFICIAL INTELLIGENCE TRANSFORMATION IN MODERN BANKING ECOSYSTEMS: STRUCTURAL, OPERATIONAL, AND GOVERNANCE CHALLENGES

Abstract

The objective of this study is to investigate the comprehensive integration of artificial intelligence within modern banking and financial ecosystems by analysing its profound structural, operational, and macroeconomic implications. Drawing on recent empirical literature and institutional data, the work examines how AI technologies—ranging from machine learning algorithms to agentic workflows and specialised small models—are transforming retail banking, risk management, credit scoring, and regulatory compliance. The research shows that while AI adoption significantly improves operational efficiency, default prediction accuracy, and customer personalisation, it also introduces complex governance challenges, including algorithmic bias, data privacy vulnerabilities, and shifts in profit pools. The analysis also evaluates AI's dual role in fostering sustainable green finance and corporate ESG integration, alongside its paradoxical capacity to influence shadow banking dynamics. By proposing an integrated technological and governance framework featuring zero-trust architecture and explainable artificial intelligence, this study outlines a strategic roadmap for financial institutions transitioning toward resilient, secure, and highly automated digital ecosystems.