Personalization at Scale: Banks can’t wait

Banking is a serious business. It manages the money people save, transact, and borrow, decisions that carry huge value and impact our everyday lives. While other sectors embrace artificial intelligence (AI), many banking customers remain wary, intimidated by technology advancements, overwhelmed by financial options, and anxious about cyber fraud. That is why banks of the future must evolve into enablers of their customers’ financial well-being, moving away from being product-centric to becoming customer-centric partners that utilize technology to simplify and support people’s financial lives. With AI, banks can go even further by dynamically customising products, such as features, rates, and offers, to meet customers’ individual needs and behaviors.

This is no longer about segments or cohorts. It’s about understanding each customer as an individual in real time. Banks must deliver what customers need when they need it. People already experience this level of personalization elsewhere. They wonder why banks keep treating them like strangers. Personalization should shift from a feature to the foundation of customer experience. It must be woven into the core of banking strategy.

The stakes are significant. McKinsey notes that unlocking personalization at scale could generate between $1.7 trillion and $3 trillion in global value for banks. This value comes through higher revenues and cost efficiencies. Here, we explore how personalization becomes the foundation of the customer experience. We’ll examine how it evolves across four levels.

What is level 1 personalization?

At this baseline, users get a standard, well-designed digital experience. It’s simple, reliable, and easy to use.

The bank acts as a guardian of finances in this stage. It ensures a smooth experience across devices and interactions. That means:

• Making frequent tasks like bill payments and transfers quick and easy.
• Simplifying onboarding and other complex actions.
• Designing responsive layouts that adjust to device type and screen size.
• Building adaptive interfaces, such as a desktop list-plus-details view and a single-focused item on mobile.

This stage focuses on usability across scenarios. It provides the baseline for future personalization.

Level 2: How does segmented personalization work?

Level 2 groups users into broad categories. These categories are based on demographics, location, or other shared characteristics. It’s “one-to-many” customization.

The bank acts as a steady partner here. It delivers a consistently engaging experience. Examples include:

• Personalized landing screens: The app dynamically reorders the homepage based on behavior. It prioritizes frequently used services. A cardholder who checks credit-card actions daily sees that widget first. A freelancer might see expense tracking.
• Senior-friendly modes: Simplified interfaces with larger text and less clutter. Clearer navigation for older users, similar to WeChat “Easy Mode”.

This level designs for flexible use across devices and contexts. It follows progressive enhancement principles. The goal is to provide an appropriate experience while keeping it familiar and intuitive. Studies show that banking customers of all ages want a more personalized experience, according to a report by Ermarketer. Citing a 2024 Harris Poll, 74% of customers say they want more personal experiences from their banks, while 66% are comfortable with banks using their data to personalize their experiences.

Level 3: What makes behavioral personalization different?

Level 3 personalization becomes dynamic. It adapts to individual actions and recent transaction patterns. This one-to-some personalization relies on profiles and behavioral data.

The bank becomes a facilitator of financial health. It uses insights to help customers make better money choices. Examples include:

• A customer who spends on travel receives a travel card offer with cashback and FX fee waivers rather than a generic promotion. In this case, the bank recommends a personalized product that aligns with the customer’s income, spending category, and lifestyle based on pKYC and transaction history.
• A user who pays off a loan or receives a salary increase receives a personalized message, such as: “Congratulations! You’ve just cleared your loan / received a pay rise, would you like to move that extra cash into a high-yield online savings account?”

This level emphasizes authenticity and trust. It shows that the bank supports the customer’s financial well-being. As McKinsey notes, 71% of consumers expect companies to deliver personalized interactions, and 76% are frustrated when this fails to happen.

Level 4: Why is contextual personalization the future?

The highest level focuses on tailored content and recommendations. It provides dynamic information based on current events and location. Time of day and device used also influence the banking services offered.

The bank acts as a trusted partner here. It anticipates needs and offers personalized experiences. These include nudges, recommendations, and pre-approved credit offers. Repayment negotiations are also part of this level. Examples include:

• “Your Spotify bill has increased by €3, do you want to check your subscription?” or “We’ve spotted duplicate payments to vendor X, would you like to dispute this?”
• A more sophisticated scenario could be offering a flexible credit line to a small business owner showing cash-flow strain.
• A retail customer has maxed out their credit card at Home Depot and clearly needs money for home renovations that the bank could provide.
• A loyal retail customer is shopping for a new car but doesn’t want to fill out a loan application. Their bank gives them a pre-approved loan at a personalized rate.
• Banks have access to a wealth of data to identify early signs of financial stress. By proactively contacting customers to discuss repayments, they can de-stress portfolios and preserve positive relationships.

This level evolves toward proactive, human-centered journeys. These journeys build lasting loyalty.

Hyper-personalization represents the most promising frontier. Yet it’s also the most challenging. It demands combining behavioral data, contextual data, and declared preferences. The goal is to create experiences that feel helpful rather than intrusive.

However, just 4% of banks are currently scaling AI to achieve this level, according to a report by BCG. The remaining 96% are either not ready for hyper-personalization or are stuck in pilot mode, running experiments that never scale and treating AI as a feature bolted on rather than integrated into their business strategies and core systems.

How can banks start their personalization journey?

Timing and context are key from a customer’s perspective. They guide personalized journeys toward successful outcomes. Banks should recognize when customers make important financial decisions. They must offer relevant, personalized solutions with clear calls to action.

Success doesn’t come from a single, large transformation. It grows from starting small and learning fast. Personalization is iterative, not a one-time change. Digital banking can feel impersonal. Advances in machine learning and generative AI can change that. They turn transaction data into continuous, meaningful conversations.

The path forward involves moving from siloed systems to connected ecosystems. Processes and communications must be integrated. It means shifting from a product-centric model to a solution-centric approach. Banks should offer tailored advice and dynamic pricing rather than static offers. These shifts make financial services more relevant and personal. They benefit all customers at every life stage.

Explore more on this theme in our podcast episode, Hyper-personalization: The key to customer growth in banking.

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