Asian Journal of Advanced Research and Reports

The global banking industry is undergoing structural change as artificial intelligence becomes integrated across front-, middle-, and back-office functions. This study examines how AI is modernising banking operations and reshaping institutional priorities beyond traditional performance determinants such as credit risk, capital adequacy, macroeconomic conditions, and information and communication technology investment. A systematic review and qualitative synthesis were conducted using recent academic literature, industry reports, and secondary expenditure data for banking institutions in the Americas and Europe. The findings indicate that AI supports customer personalisation, fraud detection, cybersecurity, data management, risk assessment, compliance, decision-making, and operational automation. However, current investment priorities remain predominantly defensive, with institutions emphasising resilience, governance readiness, and risk mitigation rather than immediate profitability. The analysis further shows that adoption varies across banking functions: technology, data management, digital channels, finance, treasury, risk, and compliance exhibit stronger effects, whereas sales and other relationship-centred activities remain comparatively dependent on human judgement. Rapidly increasing expenditure therefore does not automatically produce a measurable productivity dividend. Effective implementation requires organisational maturity, workforce reskilling, reliable data architecture, explainable systems, ethical oversight, and regulatory alignment. The study concludes that AI-driven banking modernisation is not solely a technological transition; it is an institutional transformation whose long-term value depends on balancing algorithmic capability with accountability, transparency, cybersecurity, and trust.