What Is Hyper-Personalisation in Banking — And What It Takes to Scale It Today

Every bank claims to “know” its customers. But if you’ve ever received an irrelevant offer—a student loan when you’ve long graduated, or a mortgage email just after buying a house—you’ll know the truth: most personalisation still feels anything but personal, falling short of the true promise of hyper-personalised banking.

Banks have made strides in tailoring experiences, but many are stuck at the surface. They greet customers by name, segment by age or income, and push product offers based on broad categories. While these tactics look good on paper, they often fail to resonate in the moments that matter—and fail to scale beyond isolated campaigns.

And the numbers back this up. Global Banking & Finance found that 71% of banks run personalised campaigns, yet fewer than half use customer data, AI, or behavioural signals effectively. According to Rivel Banking Research, only 48% of customers feel understood by their bank, and just 37% say they receive personal advice regularly.

In a world where customers expect the same seamless, personalised experiences they get from Spotify or Netflix, this gap is widening fast. And as customer bases grow, the complexity of delivering relevant, timely interactions grows with it—turning personalisation from a marketing challenge into a full-scale operational one.

This is where hyper-personalised banking raises the bar. By harnessing AI, real-time data, and behavioural insights, banks can create dynamic, context-aware journeys—ones that respond instantly to a customer’s needs, preferences, and even their unspoken intentions.

Imagine a banking experience that detects when financial habits shift, automatically suggests smarter products, or adapts onboarding flows on the fly—without manual intervention.

This isn’t a distant ambition; it’s already happening in pockets. But the real challenge—and the true differentiator—is scale: how to deliver this level of intelligence and relevance to millions of customers, consistently, seamlessly, and cost-effectively.

In the next section, we’ll explore real-world examples of how banks are applying hyper-personalisation—and what they reveal about overcoming the scaling challenge.

What Are Examples of Hyper-Personalisation in Banking?

Delivering a scalable hyper-personalised banking experience requires more than just good intentions—it demands robust systems that can deliver tailored journeys consistently, across millions of interactions.

DBS Bank offers a strong example. Its AI system acts as a 24/7 financial advisor, analysing behaviour to deliver tailored advice—like flagging idle cash in low-interest accounts and suggesting smarter investments. DBS now delivers two million personalised advice pieces monthly, demonstrating how hyper-personalisation can be scaled sustainably across a vast customer base.

The Commonwealth Bank of Australia (CBA) uses AI to enhance customer experience and streamline operations, cutting call centre wait times by 40% and scam-related losses by 50%. AI also supports commercial lending, helping CBA manage more clients without increasing staff—proving that personalisation at scale can drive both efficiency and profitability.

But hyper-personalisation isn’t just about strategy—it’s about embedding it into high-volume, everyday interactions, which is essential for scale. Here’s what that looks like:

• Onboarding that Adapts in Real Time: A new customer starts signing up on mobile, then switches to desktop. The process picks up seamlessly, adjusting to their behaviour and device. 
• Proactive Product Offers: A credit card user’s spending shifts to travel. The system detects the change and recommends a rewards card offering better value—before they start looking. 
• Context-Aware Outreach: A customer browses loan FAQs late at night. By morning, they receive a personalised in-app message with a pre-approved offer and direct access to an advisor. 
• Re-Engagement Journeys: A previously active app user slows down. The system launches a tailored re-engagement campaign with incentives based on their past preferences.

These are not isolated campaigns or manually triggered journeys—they are the foundation of hyper-personalised banking, powered by AI engines that interpret data in real time and respond with precision.

While these examples highlight what’s possible, the reality is that many banks struggle to reach this level of sophistication consistently. In the next section, we’ll explore why—breaking down the key challenges that keep banks from scaling hyper-personalisation effectively.

What’s Behind the Scaling Struggles of Hyper-Personalised Banking?

If hyper-personalisation is already proving its value, why aren’t more banks delivering it at scale?

The answer isn’t a lack of ambition—it’s the barriers that stand in the way. Many banks start with strong personalisation goals but quickly hit roadblocks that stall progress—making true hyper-personalised banking difficult to achieve.

Time and again, the same hurdles emerge—creating friction, slowing momentum, and preventing personalisation from reaching its full potential:

• Siloed Data: Many banks still operate with fragmented data systems. Without a unified view of the customer, personalisation efforts become disjointed, leading to inconsistent messaging across channels. 
• Legacy Systems: Outdated core banking platforms and rigid workflows make it difficult to support dynamic, real-time interactions. Even when new tools are layered on top, integration gaps slow down execution. 
• Manual Processes: Too often, personalisation remains campaign-driven—set up in advance and fixed in scope—rather than dynamically adapting to real-time behaviours and events. 
• IT Bottlenecks: Business teams struggle to move quickly because every change, test, or new journey requires tech support. This slows innovation and limits agility. 
• Compliance Fears: With growing scrutiny over data privacy and fairness, some banks take a cautious approach—delaying or scaling back personalisation initiatives to avoid regulatory risk.

These aren’t isolated campaigns—they’re powered by AI engines that analyse data in real time, responding to behaviours with precision and relevance.

While these examples show what’s possible, most banks struggle to achieve this level consistently. In the next section, we’ll explore the key challenges holding them back from scaling hyper-personalisation effectively.

How to Scale Hyper-Personalisation—And Get It Right

Scaling hyper-personalisation isn’t just about adopting new technology—it requires a shift in how banks organise their data, workflows, and teams. Many initiatives start strong but falter when they hit operational or technical barriers—proving just how challenging delivering a scalable hyper-personalised banking experience can be.

So how do you move from ambition to execution? It starts with a clear, structured approach to scaling hyper-personalisation—one step at a time.

Step 1: Start with a Clear Focus

One of the most common pitfalls is trying to personalise everything at once. It’s far more effective to begin with a single, high-impact journey—like onboarding or loan cross-selling—where value can be proven quickly. This focused approach reduces risk, allows teams to learn fast, and builds confidence before wider scaling begins.

Step 2: Build the Right Data and Compliance Foundation

Personalisation can’t succeed without unified, real-time data. Banks need infrastructure that consolidates behavioural signals across systems to enable timely, context-aware engagement. At the same time, compliance must be embedded from the outset—privacy-by-design frameworks, consent management, and auditability ensure personalisation can scale securely and sustainably.

Step 3: Operationalise AI-Driven Orchestration

AI must move beyond reporting to power real-time decisioning across journeys. By embedding AI into the operational flow, banks can trigger and adapt interactions automatically—transforming personalisation from static campaigns into dynamic, evolving experiences that respond instantly to customer behaviours.

Step 4: Enable Agility Across Teams

To keep personalisation responsive, business teams need the ability to act quickly without relying on IT. No-code and low-code platforms allow marketing, CX, and product teams to design, test, and optimise journeys independently. Embedding continuous optimisation—through real-time tracking and AI-driven testing—keeps experiences fresh and effective over time.

Ready to Take the Next Step?

While these steps provide a solid framework, success depends on partnering with a provider that can translate strategy into seamless execution—ensuring scalable hyper-personalised banking is both achievable and sustainable.

Schedule a free consultation with JurisTech today and discover how our AI-driven solutions can help you turn strategy into action.