Digital transformation: Smaller banks struggle with costs and scale when adopting large banks’ digital strategies

Smaller banks often overlook the fact that digital transformation is not merely about procuring technology. “They see what the largest banks have deployed and attempt to replicate it, only to discover that those solutions were purpose-built for different operating models, different customer volumes, and different cost structures,” says Sundararajan S, Co-founder & CEO of i-exceed in an exclusive interaction with Bizz Buzz

What is the everyday friction points that frustrate customers the most?

The frustrations haven't changed fundamentally in decades—they've simply migrated to digital channels. Customers still struggle with basic tasks like disputing transactions, updating beneficiaries, or understanding why a transfer was declined.

What's different now is their diminished tolerance for friction. The core issue is fragmentation. A customer might start a process on mobile, continue on desktop, and finish with a phone call, only to discover the information doesn't travel with them.

Many banks have digitized individual touchpoints without digitizing the underlying workflows. The result is digital theater - interfaces that look modern but still require multiple logins, repetitive data entry, and unexplained delays.

The second pain point is opacity. When something goes wrong, customers receive generic error messages with no clear path to resolution. They don't know if they need to wait ten minutes or ten days, or whether they should call someone or try again later. This ambiguity creates anxiety that erodes trust more than the actual problem does.

In practice, fixing this quickly often means redesigning the workflow end-to-end rather than polishing touchpoints - a cross-functional sprint that removes duplicated data-steps typically reduces calls and rework by a measurable percentage.

What experience shows is that customers don't expect perfection. They expect clarity, continuity, and control. When we've addressed these three elements—even with nascent technology—satisfaction improves dramatically. The technology matters less than the coherence of the experience we build with it.

Why smaller banks struggle when they try to copy large-bank digital strategies?

Smaller banks often overlook that digital transformation is not merely about procuring technology. They see what the largest banks have deployed and attempt to replicate it, only to discover that those solutions were purpose-built for different operating models, different customer volumes, and different cost structures.

The fundamental difference isn't capability—it's context. Large banks can justify significant investments in custom platforms because they're spreading costs across millions of customers. They have dedicated teams for each component of the digital ecosystem. Smaller banks attempting the same approach end up with expensive systems they can't fully utilize and don't have the staff to optimize.

More critically, smaller institutions often possess advantages they inadvertently surrender when copying larger competitors. Their customers frequently value relationship continuity and personalized service over feature breadth. When a community smaller bank tries to mimic a megabank's digital experience, they risk becoming an inferior version of something their customers weren't seeking in the first place.

The successful approach I've observed involves selective adoption. Smaller banks should automate the routine and commoditized interactions—balance inquiries, bill payments, routine transfers—while preserving human engagement for complex situations where their relationship advantage matters. This hybrid model plays to their strengths rather than competing on terrain where they're structurally disadvantaged. The goal isn't digital parity; it's strategic differentiation.

How AI can be layered onto legacy systems realistically?

The prevailing narrative suggests banks must choose between wholesale system replacement or technological stagnation. Reality offers a middle path that's more pragmatic and less risky.

AI's value in banking isn't replacing core systems—it's creating an intelligent interface layer between those systems and customers or employees. AI can function as an adaptive orchestration layer that interprets intent, harmonizes data outputs from varied operational engines, and enables real-time decision support without forcing banks into disruptive core replacement projects.

Consider a practical example: Many banks have customer data scattered across loan origination systems, deposit platforms, and CRM tools that don't communicate well. Rather than replacing these systems, which will be a multi-year, high-risk endeavour, digital banking solutions can deploy AI that queries these systems simultaneously, reconciles the information, and presents a unified view. The customer experiences seamless service, the bank gains a coherent operational layer that accelerates service delivery and reduces dependency on brittle point-to-point integrations.

The second opportunity is in exception handling. Legacy systems generate countless situations requiring manual intervention—mismatched data fields, incomplete applications, or transactions flagging outdated rules. AI can triage these exceptions, resolve straightforward cases automatically, and route complex issues to appropriate specialists with relevant context already assembled.

It yields measurable wins fast while allowing an orderly technical roadmap to continue. It's not the most elegant solution architecturally, but it's the most realistic one operationally for institutions that must maintain service continuity while modernizing.

How banks are moving from reactive service to predictive Customer experience?

The shift toward predictive customer experience represents a maturation in how we think about data. For years, banks have collected extensive information but primarily used it for risk management and regulatory compliance. Banks are now repurposing that data to anticipate customer needs rather than merely respond to them.

The sophistication varies considerably. At the simpler end, banks are alerting customers before problems occur—flagging unusual spending patterns that might indicate fraud, or notifying them before an automatic payment fails due to insufficient funds. These interventions prevent frustration rather than apologizing for it afterward.

More advanced applications involve life-stage anticipation. When spending patterns, life events, and financial behaviours suggest someone might be preparing to purchase a home or start a business, digital banking solutions can proactively offer relevant guidance and products. What sets predictive customer experience apart today is combining behavioral signals with contextual relevance, offering the right insight or support exactly when the customer is most likely to act on it, not just when it’s convenient for the bank.

The organizations succeeding here share a common characteristic: they've shifted from campaign-based banking to continuous listening. Rather than periodic promotional pushes, they maintain ongoing awareness of each customer's financial trajectory and intervene at moments when assistance genuinely adds value.

This requires new governance, new KPIs and a ritual of continuous feedback, not another campaign calendar. The core challenge isn’t just tools or models — it’s embedding a customer-first operating rhythm where insights drive moments of value rather than product-centric milestones, reshaping decisions across the enterprise.