For decades, truly personalised financial services have been reserved for a select few, with private banking clients benefitting from bespoke lending structures and tailored advice. But, in the age of AI, where everyone carries around personal assistants in their pockets, consumers are demanding the same level of personalisation from their banks.
72% of retail banking customers say personalisation influences their choice of bank. In practice, this means they expect lending rates to align with real-time affordability. Consumers want flexible repayment options that can accommodate changes in incomes, and proactive prompts that encourage saving based on spending patterns or prevent costly overdrafts.
To retain existing customers and attract new ones, retail banks must focus on delivering hyper-personalised services to help their customers build wealth. If not, they will watch customers migrate to providers who will. However, personalisation at scale is no easy task.
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The data gap behind the personalisation promise
Most consumer banks still have a long way to go to provide a hyper-personalised experience. While private banks can hire managers to deliver bespoke services to a wealthy few, consumer banks must rely on data to deliver personalised services at scale. However, burdened with outdated legacy systems, most banks cannot access the real-time data required to understand each customer and their needs.
Built for a different era of banking, legacy core systems rely on batch processing, static data models, and fragmented architectures that make it difficult to maintain a single, 360-degree view of the customer. Banks often make decisions based on delayed or incomplete data, and cracks are beginning to show. Recent data issues at major UK banks demonstrate how legacy systems are struggling to maintain consistency and accuracy at scale, with lasting impacts on customer confidence.
At the same time, attempts to retrofit legacy core systems for real‑time data, along with workarounds to compensate for their limitations, introduce new risks. In lending, for example, teams often extract customer data into Excel spreadsheets, model different scenarios manually, and then re-enter those decisions into core systems. While this may enable some level of personalisation, it is slow, resource-intensive, and error-prone. More importantly, it undermines data integrity and auditability, increasing both operational and regulatory risk.
Building a strong data foundation that offers granular insights on every customer is critical for the next wave of innovation. AI-driven personalisation depends on high-quality, real-time data to generate accurate, context-aware recommendations.
A pragmatic path to scalable, controlled personalisation
With legacy infrastructure posing a major barrier to innovation, banks must rethink how to access and act on data in real-time. By taking a cloud-native approach to their banking architecture, banks capture every transaction and interaction as it happens, building a comprehensive, reliable view of each customer. As a result, banks can identify real customer needs and deliver personalised services, from tailored lending decisions to context-aware financial insights, without compromising on accuracy or control.
However, for most established banks, the idea of replacing their core systems outright remains unrealistic. These platforms are relied on by millions of customers and underpin critical business processes, making a straight switchover too risky and disruptive to even attempt. The challenge then becomes not just what to change, but how to change it.
This is why a growing number of firms are adopting a dual-core strategy. By running a cloud-native core alongside existing legacy systems, banks can start innovating in a controlled environment. New products and personalised services can be developed, tested, and refined without impacting the stability of core operations. Over time, functionality can be migrated gradually, reducing risk to customer operations while modernising core systems.
This approach also allows banks to demonstrate value early with bite-sized projects and win over leadership with concrete evidence. Rather than committing to large-scale transformation programmes, teams can use smaller project successes to demonstrate how hyper-personalisation improves customer outcomes, unlocks new revenue streams, and contributes to business objectives.
From expectation to execution
The demand for hyper-personalisation is already here, and it is only intensifying. Customers increasingly prefer providers that can deliver relevant, proactive, and tailored experiences. The risk for banks is not simply falling behind, but losing meaningful customer relationships, the revenue they generate, and the opportunity to attract new ones.
Meeting this challenge requires more than incremental change. It requires recognising that hyper-personalisation is not just a front-end ambition, but a data and infrastructure capability. Banks that invest in real-time, event-driven foundations – and adopt pragmatic approaches to modernisation – will be able to move from a pipedream to reality.
Hyper-personalisation doesn’t have to be a luxury reserved for the few. With the right infrastructure in place, it can become the standard for all. The technology to achieve this already exists. What remains is the willingness to embrace it.
Steve Round, Co-Founder of SaaScada
