Why banks struggle to understand their customers

In today’s banking world, change is happening fast. On one side, digital-only banks are winning younger, tech-savvy customers, offering easy account setups, transparent pricing, and sleek digital experiences. On the other side, traditional banks still manage extensive branch networks, complex product portfolios, and large staff operations built around in-person service. Between these two realities lies a widening gap in customer understanding. 

Many banks believe they know their customers well because they have decades of account data, long-standing relationships, and deep knowledge of financial behaviour. But understanding customers today requires more than transactional data. It means understanding emotions, preferences, expectations, and life moments. And this is where traditional institutions often struggle. 

In this article, we explore why so many banks find it difficult to truly understand their customers, and how AI-driven insight can help bridge the gap between legacy models and modern expectations. 

The limits of legacy thinking in traditional banking 

Traditional banks grew up in a world where relationships were local and physical. Customers visited branches, talked to advisors, and built trust through personal connections. The bank manager often knew the customer’s family and financial history. That intimacy, however, came at a cost. Running branches, maintaining staff, and managing cash operations were expensive. 

As digital channels emerged, efficiency became the priority. Banks closed branches, automated service tasks, and replaced local expertise with centralised call centres and digital portals. This made operations leaner, but it also created distance. In the pursuit of efficiency, many institutions lost the human insight that once helped them understand their customers on a personal level. 

At the same time, customer expectations evolved. People no longer compare banks only with each other; they compare them with every other digital experience they use. If streaming services and e-commerce platforms can anticipate needs and offer personal recommendations, why can’t banks? 

Understanding banking customers across generations

Understanding customers has become even more complex because banking now serves two very different groups: the digitally native and the digitally cautious. 

Younger customers have grown up online. They expect quick account openings, instant transfers, transparent fees, digital-first support, and a modern digital banking experience. They are also highly willing to switch providers if they find a better deal or experience. Challenger banks and fintech startups cater directly to this group with modern design, gamified interfaces, and low-cost products. 

Older customers, however, still value personal contact. Many feel left behind by overly simplified digital interfaces that remove detail or guidance. They may distrust mobile-only banks and prefer to speak to someone before making financial decisions. For this group, clarity, information, and reassurance are key. 

Traditional banks face a dilemma: how to modernise their services without alienating the customers who still rely on personal relationships. Many institutions respond by building digital layers on top of legacy systems, but these often fail to deliver a seamless or personalised experience. The result is a patchwork of systems that serve everyone but truly satisfy no one. 

Data, transparency, and the trust deficit in banking customer experience 

Banks have no shortage of data. They track transactions, savings, loans, and investments for millions of customers. Yet despite this, many still struggle to translate data into understanding. Much of the data sits in silos, separated by department, system, or region. Marketing might know what customers click on, while the service team knows what they complain about, but rarely do these insights meet in one place. 

Transparency also remains a challenge. Fees, terms, and product conditions are often hidden behind complex wording or scattered across multiple pages. Comparison portals make it easier than ever for customers to identify better deals, putting pressure on banks to simplify and clarify their pricing. In a market where transparency equals trust, lack of clarity can quickly become a barrier to loyalty. 

Understanding customers requires connecting not just data, but meaning. Banks need to know why customers behave as they do, not just what they do. They need to understand how customers feel about fees, service, and accessibility. This emotional dimension of experience has historically been overlooked, but it is crucial to building genuine trust. 

Why traditional segmentation models fall short in modern banking 

Part of the problem lies in how many banks still think about segmentation. Traditional models group customers by age, income, or product type. These categories are useful but static. They fail to capture changing behaviours or emotional needs. Two customers of the same age and income might have completely different financial goals and service expectations. 

Banks also tend to focus more on managing risk than understanding needs. Their systems are designed for compliance and control, not for discovery or empathy. This conservative culture, combined with legacy technology, makes it difficult to adapt quickly to customer feedback or emerging trends. 

Adding to this is the internal shift toward cost optimisation. Many banks have replaced customer relationship managers with IT consulting roles, aiming to digitise interactions. While this brings efficiency, it often reduces the opportunity for meaningful, two-way communication. The result is a digital customer who feels known by their account number, not by their needs. 

How AI improves customer understanding in banking 

Artificial intelligence offers a way forward. Done right, AI can reintroduce the understanding that banking once had, but at scale and with data-driven accuracy. Instead of relying on static segmentation, AI can identify micro-patterns in customer behaviour, helping banks understand not only what customers are doing, but why. 

AI for behavioural and sentiment analysis 

AI can analyse unstructured data such as open-text feedback, chat transcripts, and survey comments to uncover emotional drivers behind satisfaction or frustration. For example, it can identify when customers mention “fees,” “waiting time,” or “confusing process” across thousands of interactions and flag these as areas needing attention. 

This intelligence helps banks design more relevant services and communication. For younger customers, that might mean faster onboarding, better mobile UX, or real-time support. For older customers, it might mean clearer explanations, hybrid service options, or personalised guidance through digital tools. 

AI-powered predictive insights for retention 

AI also makes it possible to combine transactional and experiential data in one view. By correlating behaviour such as decreased account activity with sentiment such as frustration over unclear fees, banks can predict potential churn and intervene before customers leave. 

Learn more about Netigates ASK AI

Balancing innovation with trust and compliance

While AI holds promise, scepticism remains strong in banking. Decision-makers worry about compliance, explainability, and public perception. Regulators demand transparency in how algorithms make decisions. Customers, too, may fear bias or misuse of personal data. 

The path forward is responsible AI, systems that are transparent, explainable, and aligned with strict data governance principles. When built with privacy and compliance in mind, AI can become an ally rather than a threat. It can help banks meet DORA and ISO requirements, ensuring that insights are secure and auditable while enhancing customer understanding. 

Transparency is key. When banks show customers that AI is used to improve service rather than to sell more aggressively, trust increases. When communication is clear about how data is used, adoption grows. In short, AI should serve as a tool for empathy and efficiency, not just automation. 

Designing digital banking experiences for every generation 

AI is not only about analytics; it’s about accessibility. One of the greatest challenges in banking is making digital services intuitive and inclusive for all age groups. A clean user interface may appeal to young customers, but it can alienate older ones if it removes too much context or information. Here, AI can support adaptive design – interfaces that simplify complex information without losing clarity. 

AI-driven personalisation can also help older customers navigate online banking more confidently. For example, smart assistants can guide users step by step through transfers or bill payments, ensuring that they understand every action before confirming it. Meanwhile, younger users can receive real-time insights about spending habits or investment opportunities. 

The future of banking experience lies in combining human empathy with AI-powered intelligence. Banks that achieve this balance will not only serve their existing customers better but will also attract new generations seeking security, transparency, and personal relevance. 

Using AI to transform customer understanding in banking

Banks have always been in the business of trust. But in today’s digital world, trust is no longer built in the branch; it is built through data, transparency, and understanding. Traditional models, while still valuable, cannot fully capture the complexity of modern customers. 

Artificial intelligence offers banks the opportunity to bridge that gap. By combining secure data management, sentiment analysis, and adaptive personalisation, institutions can finally see their customers as people rather than profiles. They can balance efficiency with empathy and modernise without losing the human touch. 

In the end, the banks that thrive will be those that use AI not just to optimise operations, but to understand the people behind the accounts, and to turn that understanding into meaningful, trusted relationships that last.