AI as a Growth Engine for Banks and not Just an Efficiency Tool
The following use cases illustrate how AI is unlocking new avenues for profitability and sustainable growth across the banking ecosystem: One of the most transformative frontiers is credit risk analysis. According to Motilal Oswal’s Banking Report, banks have prioritized asset quality in the last few years by tightening credit filters and raising CIBIL thresholds, particularly in retail lending. This has helped reduce non-performing assets (NPA) but also limited growth. Agentic AI is redefining this equation, enabling banks to perform deep, data-rich assessments across structured and unstructured data, auto-generate personalized contracts, and track asset performance in real time. These systems are immune to bias and capable of early risk detection, empowering banks to take more informed, calculated risks without compromising asset quality.
Customer Engagement
AI is revolutionizing customer engagement through user-specific marketing by transforming vast amounts of customer data into actionable insights. This enables banks to deliver hyper-personalized offers, timely promotions, and product recommendations tailored to individual preferences and behaviors. Such data-driven communication not only enhances campaign effectiveness but also builds trust and fosters long-term loyalty. Underpinning this transformation are machine learning models such as Logistic Regression, Decision Trees , K-Nearest Neighbors , etc which can be applied to banking datasets to predict customer behaviors, including the likelihood of churn. By identifying at-risk customers early, banks can proactively engage them with personalized retention strategies, turning potential losses into opportunities for deepened relationships.
Regional language support
Another significant opportunity lies in regional language support. India, with its 121 spoken languages, still struggles with financial inclusion due to linguistic barriers. A 2023 RBI report shows that 22% of Indian adults remain unbanked and 11% are underbanked, and only 36% of rural users are comfortable banking in English or Hindi. A recent dispute between a bank executive and their customer underscored the urgency of the issue. AI-powered voice platforms are now capable of recognizing over 100 dialects with 95% accuracy ( study done by IIT Madras in 2024), can enable multilingual support across customer service, digital interfaces, and documentation making inclusion a lived reality.
Re-Imagining banking infrastructure
AI is reimagining banking infrastructure. As mobile wallets and digital banking surge, 83% of Gen Z prefers digital-first financial services, branches now play a secondary support role. AI is accelerating this transition by automating onboarding, service requests, and customer engagement at scale, minimizing dependence on physical infrastructure and unlocking significant cost saving for banks.
Trough of Disillusionment
As generative AI enters the Trough of Disillusionment in Gartner’s Hype Cycle report of 2025, banking leaders may find themselves questioning its real value amidst unmet expectations and complex implementation hurdles. Yet, this moment of reckoning is precisely where long-term transformation begins, when hype fades and clarity sets in.The key is to move past inflated expectations and instead focus on AI maturity, skilled execution, and use-case precision. For banking leaders, the road through the Trough is not just about surviving disillusionment, it’s about using this phase to build durable, scalable AI capabilities that drive both profitability and purpose.