The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) in financial services has fundamentally transformed decision-making, risk assessment, customer engagement, and operational efficiency across global banking and financial ecosystems. This study proposes an advanced structural and performance evaluation model for sector-wide adoption of AI and ML technologies in financial services. The research synthesizes existing literature on AI-driven financial transformation, identifies critical adoption determinants, and develops a conceptual framework for evaluating technological, organizational, and regulatory readiness.

Drawing on systematic literature from fintech innovation, algorithmic finance, and banking digitalization, this paper highlights the dual role of AI as both an enabler of financial efficiency and a source of governance complexity. Prior research emphasizes that AI enhances predictive accuracy in credit scoring, fraud detection, and portfolio optimization while simultaneously introducing challenges related to transparency, bias, and regulatory compliance (Cao, 2020; Duan et al., 2019). Furthermore, AI’s strategic implications in financial systems are strongly influenced by public policy frameworks and institutional readiness (Bredt, 2019).

The proposed model integrates structural dimensions (data infrastructure, algorithmic capability, and system interoperability) with performance dimensions (efficiency, accuracy, scalability, and risk mitigation). The study contributes to the growing discourse on AI-driven financial transformation by offering a consolidated analytical framework that supports both academic understanding and practical deployment strategies.