Abstract:

India’s financial services sector has moved from branch-centred and rule-based processing toward digitally mediated, data-intensive services in which artificial intelligence (AI) and machine learning (ML) increasingly support detection, prediction, personalisation and decision-making. The research problem is that public discussion often treats digitalisation, automation and AI adoption as interchangeable, while nationally comparable measures of institution-level AI adoption remain limited. This study addresses that gap by examining the transition from conventional automation to AI-enabled intelligence using verifiable secondary data and documented Indian financial-sector use cases. The study adopts a quantitative, descriptive and analytical design based on official data from the Reserve Bank of India (RBI), the National Payments Corporation of India (NPCI) and regulatory material from securities-market authorities, supplemented by peer-reviewed academic literature. The principal quantitative series covers FY 2017-18 to FY 2024-25 for Unified Payments Interface (UPI) transaction volume and value, with the RBI Digital Payments Index (DPI) used for the comparable March 2018-March 2024 period and the RBI Financial Inclusion Index used where observations are available. Growth rates and compound annual growth rates (CAGR) are calculated, and Pearson correlation is used only for the overlapping UPI-volume and RBI-DPI observations. UPI volume increased from 915.2 million transactions in FY 2017-18 to 185.866 billion in FY 2024-25, while transaction value increased from ₹1,098.32 billion to ₹260,570 billion; the corresponding seven-year CAGRs are 113.6% and 118.4%. The correlation between UPI volume and the RBI-DPI over FY 2017-18-FY 2023-24 is 0.920, indicating strong positive association, although the relationship is not interpreted causally. The review also finds that Indian authorities explicitly document AI/ML applications in fraud detection, credit assessment, customer service, surveillance, risk management, recommendation, advisory and related functions, while stressing privacy, explainability, accountability, cybersecurity and human oversight. The study concludes that India’s transformation is not simply from manual work to automated workflows; it is increasingly toward systems that analyse patterns and generate decision support. The central implication is that future value depends on combining AI capability with data governance, explainability, resilience and responsible regulation.

Keywords:

Artificial intelligence, machine learning, financial services, digital payments, FinTech, financial inclusion, explainable AI.

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