Innovate to Elevate: How Gen AI is Redefining the Future of Banking

Artificial Intelligence has moved from being a buzzword to becoming the core driver of innovation in banking. At the Bharat Fintech Summit 2025, Mahesh Ramamoorty, CIO of Yes Bank, delivered a thought-provoking keynote titled “Innovate to Elevate: Leveraging Gen AI to Transform Banking Beyond Expectations.” His insights went beyond theory, offering a practitioner’s perspective on how AI — and especially Generative AI (Gen AI) — is shaping customer journeys, risk management, and the very fabric of financial decision-making.

Every bank today runs on a wealth of transactional and customer data spread across 10–15 different systems of record. The challenge has never been about the availability of data, but about how effectively it can be harnessed.

Gen AI provides a way to unlock intelligence from fragmented data, enabling banks to:

Create contextual, personalized experiences in customer journeys.
Use historical patterns to anticipate intent, such as payments behavior.
Feed insights into onboarding, servicing, and lending journeys for a frictionless experience.

But, as Ramamoorty emphasized, the real impact depends on defining clear use cases. Without clarity of purpose, AI risks becoming a shiny tool with limited ROI.

One of the hottest trends in banking is Agentic AI — conversational, intelligent systems that augment contact centers and CRMs. Unlike traditional automation, which handles repetitive tasks, agentic AI can:

Provide front-line agents with contextual intelligence about customer history.
Automate complex processes like reconciliations or policy lookups.
Enable personalized, self-service experiences for customers at scale.

This is a shift from AI as a back-office enabler to AI as a frontline partner in customer experience.

While customer experience grabs headlines, Ramamoorty stressed that risk management is where AI’s true power lies.

Key applications include:

Fraud detection & prevention using transaction patterns.
Backtesting and policy validation, reducing the manual load of credit risk reviews.
Enhanced information security, protecting institutions from increasingly AI-powered cyber threats.

As financial institutions digitize, explainable AI will become critical. Customers and regulators alike demand transparency — if an AI denies a loan or flags a transaction, it must provide a clear rationale, not a “black box” answer.

Perhaps the biggest roadblock for banks lies in training AI responsibly. Unlike tech companies, financial institutions are custodians of highly sensitive data. This means:

They cannot freely feed customer data into public large language models.
Success rates in early pilots remain modest (20–25% in some Yes Bank experiments).
Biases and hallucinations pose reputational risks if not managed.

To overcome this, Ramamoorty proposed an innovative idea: creating cohorts of banks and financial institutions that could collaborate on “safe-to-share” data pools for training. This would allow AI models to learn from broader datasets while maintaining regulatory compliance.

Ramamoorthy also predicted a paradigm shift in mobile banking. Instead of managing multiple apps, future banking experiences could evolve into chat-first interfaces powered by AI. Customers would simply use natural language prompts — “What’s my EMI status?” or “Block my card” — and the system would handle the rest.

This evolution means:

Fewer apps cluttering devices.
More intuitive, conversational interactions.
Relationship managers and branch staff empowered with AI insights in real time.

While enthusiasm for Gen AI is high, Ramamoorty issued a note of caution:

ROI takes time: Even successful use cases require 3–4 months of training for modest results.
Explainability is non-negotiable: Without it, customer trust and regulatory approval are at risk.

Security cannot be an afterthought: With even Gmail confirming AI-powered breaches, regulated entities must move faster on AI-driven cybersecurity.

The message from Yes Bank’s CIO is clear: Gen AI is not a silver bullet, but a strategic enabler. Its success depends on:

Clarity of use cases — focusing on customer journeys, servicing, and risk management.
Data strategy — ensuring training data is trustworthy, unbiased, and compliant.
Collaboration — banks working together to build shared learning cohorts.
Explainability and security — ensuring AI builds trust, not erodes it.

By 2030, banking will not be about apps or portals — it will be about intelligent, conversational, trust-driven experiences, where every decision is faster, safer, and more personal.