The power of personalisation in banking: Five key use cases

Customers expect personalisation everywhere -from online shopping to takeaway meals. The banking industry is no exception. As consumers demand seamless, tailored experiences across digital and physical touchpoints, banks must step up, delivering smarter, more intuitive interactions. From hyper-relevant product recommendations to AI-driven customer service, personalisation is no longer a luxury but a necessity. It enhances engagement, drives loyalty, and boosts revenue. While technology enables these advancements, a strategic approach ensures their success.

1. Hyper-personalised product recommendations

Gone are the days of generic product pitches. Today, banks use multiple data points -transaction history, spending habits, and life events -to offer hyper-relevant recommendations. Whether it’s a tailored credit card for frequent travellers, an investment plan aligned with financial goals, or a mortgage suggestion based on life stage, targeted offerings create meaningful engagements and improve conversion rates.

AI and data analytics refine these recommendations in real time. For example, a customer who frequently shops online might receive cashback credit card suggestions tailored to eCommerce purchases. A young professional regularly saving a portion of their salary could be offered financial planning options to help them buy their first home. By placing customer value at the core of personalisation efforts, banks enhance satisfaction and drive revenue by up to 15%.

2. Dynamic content and messaging in digital banking

Customers interact with banks across multiple channels, making consistency and relevance crucial. Personalised digital experiences - such as dynamic website content, adaptive mobile app interfaces, and targeted email communications - help banks deliver the right message at the right time. With 80% of UK customers using a banking app, many checking in daily, dynamic content significantly enhances engagement.

AI-driven automation enables banks to segment customer journeys based on individual financial goals and behaviours. A student might see content on managing student loans and building credit, while a retiree receives insights into pension planning.

However, banks must go beyond basic demographics. Not all 18-year-olds attend university; not all married couples have children. By combining life-stage indicators with granular data points, banks ensure every customer benefits from interactions relevant to their unique financial situation.

3. Predictive financial insights and advisory services

Banks are moving beyond simple transaction tracking to proactive financial advisory, leveraging AI and machine learning to analyse spending patterns, cash flow trends, and savings behaviours. This enables personalised insights, helping customers manage finances more effectively. Alerts for upcoming bills, nudges to save for specific goals, and AI-driven investment suggestions create a more supportive banking experience, positioning banks as trusted financial partners.

For instance, if AI detects that a customer regularly overspends their budget, the bank can send a gentle reminder or suggest ways to improve cash flow. A sudden large transaction could trigger a security check, reducing fraud risk. These proactive measures reinforce trust, ensuring customers feel supported while enhancing their financial wellbeing. According to research 91% of consumers are more likely to engage with brands offering personalised recommendations.

4. AI-driven customer support & chatbots

AI-powered chatbots and virtual assistants are revolutionising customer service. By integrating customer history, preferences, and real-time data, these AI solutions provide contextually relevant support. Instead of generic responses, personalised chatbots anticipate customer needs, suggest next steps, and even automate routine banking tasks like bill payments, fund transfers, or fraud alerts.

Generative AI enhances efficiency and improves customer experience across the banking value chain - but only when executed effectively. Thorough testing ensures that customers feel supported rather than frustrated. A chatbot recognising frequent queries can refine responses over time. A customer regularly inquiring about international transactions might receive proactive updates on foreign exchange rates.

If AI detects frustration in a customer’s tone, it can escalate the issue to a human representative, ensuring a more empathetic resolution. By streamlining support and reducing friction, AI-driven personalisation enhances service efficiency and strengthens customer loyalty.

5. Personalised loyalty and rewards programmes

Traditional loyalty schemes often fall flat with a one-size-fits-all approach. Personalisation enables banks to tailor rewards based on customer spending habits and preferences. From customised cashback categories to exclusive offers at frequently visited merchants, personalised rewards enhance engagement and foster deeper customer relationships.

For example, a customer who dines out frequently might receive increased cashback on restaurant spending, while a frequent traveller could earn extra points on airline tickets and hotel stays. Personalisation goes beyond demographics, focusing on what customers genuinely value. By aligning rewards with lifestyle choices, banks encourage continued engagement and create a sense of familiarity, strengthening long-term relationships. AI can also dynamically optimise rewards, ensuring they remain attractive and valuable over time.  Research indicates that personalised loyalty programmes can boost customer retention by 25%, particularly when aligned with spending habits.

The future of personalised banking

Personalisation is transforming everyday banking interactions into meaningful, customer-centric experiences. By leveraging data intelligently, banks can create hyper-relevant, engaging experiences that serve immediate needs while anticipating future ones, positioning themselves as trusted lifelong partners.

Yet only 6% of retail banks have developed an enterprise roadmap for AI-driven transformation at scale. There is significant work to be done, and those that embrace personalisation will differentiate themselves, strengthen customer relationships, and maintain a competitive edge.