The financial services sector in India has undergone substantial digital transformation in the past two decades; enhancing convenience; efficiency; and security. And AI; ML are now catalyzing a significant shift and playing a transformative role in customer servicing; automating processes for cost reduction; and for efficient regulatory compliance/reporting for Financial Institutions (FIs) in India. And with the evolution of big data; cloud computing; innovative hardware; and faster special-purpose systems; the growth of AI; ML has been faster in the last few years. The market for AI in fintech is also expanding; with its value estimated at USD $42.83 billion in 2023 and projected to grow to USD $44.08 billion in 2024 (Statista; 2024). This growth represents a compound annual growth rate (CAGR) of 2.91%; with forecasts suggesting the market will exceed USD $50 billion by 2029 (Statista; 2024). Alongside this sector-specific growth; the International Monetary Fund predicts that overall financial sector spending on AI will more than double to $97 billion by 2027; showcasing a 29% CAGR—the fastest among five major industries. At the same time; the rapid adoption of AI also introduces significant risks that financial institutions must carefully manage. These risks include ethical concerns such as AI bias; challenges in data privacy; and the potential for regulatory non-compliance. Without robust strategies to mitigate these risks; financial institutions are vulnerable to operational disruptions; legal liabilities; and reputational damage. The urgency to address AI-related risks is compounded by the fact that only 39% of financial institutions have advanced from AI/Machine Learning (ML) pilots to full production; as highlighted by a 2023 Gartner survey.The study under this ToR would produce a current state assessment of the existing AI; ML applications (use cases); analyze the drivers and inhibitors of successful AI; ML adoptions in Indian FIs; highlight potential; emerging areas/use cases for use of AI; ML; conduct a risk analysis of AI; ML adoption across different use cases; including linkages between deployment of AI; ML and the Digital Personal Data Protection Act (DPDP) of 2023; and document global best practices/use cases which are relevant for the Indian context.