Pilot to profit: Banking AI needs a platform, not just a model
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Building, not just buying: The new AI story inside India's banks and NBFCs
Artificial intelligence has become a powerful engine driving the future of financial services. Yet the Reserve Bank of India's own surveys show how thin the base still is: 67% of the 76 institutions polled by its FinTech Department were exploring at least one generative AI use case, while only 20.8% of the 612 entities surveyed separately by its Department of Supervision were actually using or developing AI1
The institutions closing that distance are not simply adopting technology. They are building custom, compliant AI and reimagining the target operating model for an AI-first enterprise, that carries ambitious sandbox models into enterprise use.
State of the union: Core challenges and differentiators
Financial institutions worldwide face macroeconomic pressure, legacy debt and a tightening regulatory net. In an era where core banking services are increasingly commoditized, intense cost pressures mean that customer experience has emerged as the primary differentiator.
For Indian banks and NBFCs, the crux of the problem and the AI opportunities centre on three critical themes: transforming customer experience, combating fraud and financial crime and reaching the underserved market.
Customer experience: Hyper-personalization is now the baseline. Intelligent automation is slashing servicing costs by shifting verification, document processing, and routine queries to machines redirecting human talent toward high-value advisory work.
Banks can deliver the right product at the exact right moment, provided their underlying orchestration is compliant by design.
Fraud and financial crime: Systemic strain is reaching a breaking point. Indian banks reported fraud amounting to ₹48,021 crore in FY26, which is a 46% year-on-year surge, despite case volumes halving as AI-enabled fraud and industrial mule networks concentrate damage into fewer, high-impact attacks.2
Defensive AI, however, is fighting back: MuleHunter.AI, the AI/ML model piloted by the Reserve Bank Innovation Hub, is designed to detect mule accounts far more efficiently than rule-based screening, and the RBI reports show encouraging results from its pilot at two large public sector banks.³
The underserved market: Institutions face immense pressure to extend credit deeper into the economy, where an estimated ₹25 lakh crore of MSME demand sits unmet.4 Unlocking this next tranche requires underwriting on alternative data.
The capability versus enterprise adoption gap
Building a smart model is only the first step. Cognizant research puts the share of enterprises reporting moderate-to-large gaps between AI ambition and capability at 63%⁵. In separate research across the Global 2000, two-thirds of leaders had yet to show measurable productivity gains and one in four had paused or abandoned AI deployments6.
The root cause of this stagnation is not a lack of technological capability, but a lack of enterprise readiness. Specifically, the main barriers are a lack of contextual knowledge of the enterprise, critical gaps in AI governance and trust, and limited workforce adoption.
Many institutions run a few proofs of concept and confuse that excitement with scalable maturity.
However, across all three core themes, an unexplainable model is an undeployable model. Under consumer protection codes, an automated credit rejection requires explainability; a customer has the right to know why a loan was denied and to request a human review.
Enabling change through a platform approach
Closing that gap means trading fragmented, artisanal experiments for a unified platform plus portfolio approach, where enterprise level AI applications are built on shared data, trust and governance, and driving change management and adoption at scale.
This enterprise-wide shift relies on three foundational pillars:
Data architecture and readiness: Compliant data infrastructure is not just about managing privacy and consent. It requires a robust enterprise data architecture with proper data definitions, semantic meaning, and ontology definitions that structurally enable the enterprise to be AI-ready.
Trust and governance: Scaling AI safely requires native explainability, enforced model cards, audit trails, and human-oversight routing. Rather than waiting for standalone AI legislation, this approach anchors AI governance in frameworks banks already answer to, providing the continuous bias monitoring and board-level policy enforcement that the RBI expects.
Change management and adoption: Democratizing AI means addressing limited workforce adoption head-on. It involves intuitive workflows that empower risk managers and analysts while maintaining quantitative rigor, turning every production decision into a training signal that the institution owns.
Financial services firms are already proving this transition is possible by utilizing comprehensive methodologies e.g. Cognizant’s Business-led Autonomous System Integration Services (BASIS) framework7, provides a business-driven framework for enterprise agentification and agentic AI transformation, helping clients move from isolated AI experiments to enterprise-scale autonomous operations.
Cognizant currently runs more than 8,000 AI engagements globally8, and its financial services business grew 12% last quarter9, as we continue to help clients close the AI velocity gap by pairing deep industry expertise with engineering, infrastructure and data modernization capabilities.
The pertinent question for banks and NBFCs is no longer which foundational model to license. It is whether their underlying enterprise platform, data architecture, and workforce are truly ready to run AI safely, profitably, and at scale.