Knowing your customers isn’t just a nice to have anymore. It’s critical for survival. AI-driven hyper-personalisation is now an expectation, shaping how financial institutions engage with clients.
AI is the core of financial personalisation
AI allows financial institutions to offer tailored products and services that go beyond one-size-fits-all. By analysing customer data, preferences, behaviours and life stages, AI provides personalised recommendations that enhance customer experience and loyalty, driving better business performance. Despite this, many institutions continue to offer standardised products, missing a real opportunity to gain a competitive edge.
The real question is – who’s doing it well? And how can you be more like them?
Read on for some best practice examples in financial services (FS), plus the benefits, challenges, and recommendations to consider for the year ahead.
So, which organisations are doing it well?
DBS Bank, Southeast Asia’s largest, runs over 800 AI models across 350 areas. But what does personalisation at DBS really mean? It goes beyond generic recommendations. By understanding individual financial habits, AI at DBS offers suggestions to reduce debt, optimise interest repayments and identify loans that suit the customer’s unique spending patterns. By analysing past spending and transaction patterns, DBS can predict upcoming payments and alert customers so they can avoid being charged fees for any shortfall in their accounts, providing real-time recommendations that deepen customer trust. 1
In Europe, Moneyhub is leading the charge in hyper-personalisation by leveraging AI-driven nudges. Using machine learning and natural language processing, Moneyhub provides tailored, proactive messaging to users, offering financial insights based on individual needs. These nudges prompt users to make better financial decisions in real-time, whether suggesting savings adjustments or alerting them to upcoming bills.2
Progressive Insurance’s snapshot programme showcases AI’s power in insurance. By tracking real-time driving behaviour through telematics, insurers can offer discounts for safe drivers. This dynamic pricing not only rewards good behaviour but also increases consumer loyalty. In a sea of insurance providers, this kind of personalisation breaks through the waves of banking and insurance offers to create real customer stickiness to a brand.3
There are clear benefits, and some challenges
AI-powered personalisation delivers greater customer satisfaction, enhanced loyalty, improved conversion rates for new customers and a stronger competitive advantage, all leading to increased profitability. “Positive sentiment and enthusiasm for AI has never been higher. 69% of retailers attribute an increase in revenue to AI; 53% of telcos believe they can build a competitive advantage with AI; and 42% of finance organisations believe they have already built a competitive advantage.” AI helps FS institutions identify cross-sell opportunities and deliver highly relevant products, adding real value. AI helps FS institutions identify cross-sell opportunities and deliver highly relevant products, adding real value.
But we can’t ignore the challenges. Hyper-personalised pricing, especially in insurance, risks undermining the principle of risk sharing, potentially creating unfair disadvantages for certain customer segments.
Bad data can lead to inaccurate predictions. Poor data governance weakens AI’s ability to make meaningful, personalised recommendations. Financial institutions need robust data strategies that continuously clean, integrate and update data to ensure their AI systems can operate effectively. Even the most advanced AI systems will fail without a strong, structured data foundation. For financial institutions, this means investing in data governance, cleaning up legacy systems and ensuring real-time data flow between systems now in parallel, or even before looking at their AI capabilities.
Privacy remains a significant concern. As AI becomes more central to customer interactions, transparency in data usage is crucial. Customers must know how their data is used and why specific recommendations are made.
To mitigate these risks, financial institutions should focus on business beyond compliance, promoting transparency and fairness in data usage, similar to the principles outlined in the EU AI Act (recognising the act doesn’t apply to the UK). Regular bias detection and auditing are essential to prevent discrimination, especially in pricing and loan approvals. Robust data governance is key to safeguarding customer data and maintaining privacy standards. Investing in explainable AI tools also allows firms to provide transparent insights into AI decisions.
What about the human touch?
While AI can process vast amounts of data and deliver hyper-personalised insights, it’s the combination of AI and human intelligence that truly elevates customer experience. Financial advisors, armed with AI-driven insights, can offer even more tailored and empathetic advice, creating a perfect balance between technology and human touch. This symbiotic relationship bridges the gap between cold, algorithm-driven suggestions and the EQ required in financial advisory services to deliver a meaningful customer experience.
Why does this all matter?
The financial institutions that embrace AI will lead the way in the next wave of customer engagement. It’s no longer just about efficiency, it’s about creating meaningful, personalised experiences that cater to every individual’s unique needs. The FS firms that get this right will reap the rewards in a world where personalisation drives loyalty.
AI has the power to transform businesses across all sectors by driving innovation and enhancing customer experiences.
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We are passionate about helping financial organisations harness the power of new and emerging technologies to shape how they better engage with their clients.
