European consumers continue to bank with traditional institutions, but many are clearly less satisfied with the experience. Earlier this year, a leading digital banking platform provider published the results of a survey of 6,300 online banking users across Europe.
Although 82 percent of respondents used a traditional bank as their main provider, 26 percent had switched banks in the past because of poor digital services, and would not hesitate to do so again. Also, 47 percent said that they would feel more satisfied if their bank improved the quality of digital services. Customers specifically called out personalized recommendations in this context.
What do customers want?
With the digitalization of banking, seamless transaction experiences have become the norm, standardized across banks with little to differentiate them. Digital channels are efficient, but impersonal. For the majority of customers, they are a way to complete quick, functional tasks such as checking account balance. This has gradually emotionally distanced consumers from their banks.
And therein lies the dilemma: The same consumers who are embracing digital banking are demanding more personal interactions with their bank. For instance, 59 percent of European consumers want financial recommendations tailored to their individual needs. Banks that fail to provide this will see customers walking away.
The good news is that artificial intelligence technologies are now enabling banking institutions to meet these seemingly irreconcilable expectations. Where previous digital technologies allowed some – mostly segment-level – personalization, AI is driving hyper-personalization of digital banking experiences, in real-time, at scale.
Personalize everything, from product recommendation to marketing communication
Advanced analytics models analyze customer and transaction data to identify insights into individual needs, preferences and behaviors, based on which intelligent recommendation engines suggest the most suitable products and services. Besides tailoring products using traditional financial metrics – think of loans and investment plans customized according to risk profile and financial goals – AI can personalize banking based on an individual’s data and activity outside the bank.
A great example is the hugely popular Buy Now Pay Later model where ecommerce businesses allow customers to pay for purchases in instalments based on their creditworthiness, assessed using previous transaction history and other variables.
AI-powered tools can also detect cash flow patterns or spending anomalies to offer proactive guidance to help every customer manage their finances better. Gen AI-powered chatbots and virtual tellers can support customers at any time of day or night, conversing in natural language just like a human relationship manager, to answer queries and even resolve complex issues.
Customers can ask specific questions about their portfolios or financial goals, and generative AI will respond independently, or along with a human advisor, with targeted recommendations and guidance.
The ability of generative AI to converse in a highly personal, human-like manner is transforming debt collection. Tapping natural language processing and machine learning, gen AI systems analyze borrower preferences and behaviors and then create personalized messages that improve both engagement and repayment. They also minimize confrontation by showing empathy, and suggesting solutions that are more acceptable to customers.
Revolutionizing communication
Coming to communication, bombarding people with generic messages is inefficient, ineffective and exposes a bank’s lack of in-depth customer understanding. It also annoys consumers who are already flooded with spam and unsolicited calls from various businesses. AI is revolutionizing communication by enabling banks to craft highly relevant messages that resonate with customers.
First, predictive tools identify which customers are likely to respond favorably to different types of messages; then generative AI swings into action, creating highly personalized content and campaigns, in the customers’ chosen language(s). Other AI solutions identify the right channel, format and time for sending the communication, making customers feel valued and truly understood by their bank.
The gaming industry also presents a compelling frontier for banks to engage younger, digitally native audiences with gen AI. With so many gamers interacting in immersive virtual environments, banks can leverage gen AI to create hyper-personalized financial experiences that align with gaming lifestyles and personas.
For example, AI-driven insights can enable banks to offer micro-investment products based on in-game spending behavior, offer dynamic credit options for digital purchases, and design savings plans that reward financial discipline with virtual incentives.
By integrating with gaming platforms and analyzing behavioral data in real time, banks can deliver contextual financial nudges—such as suggesting a budgeting tool or offering crypto-linked rewards for in-game achievements. The integration of gaming and finance, powered by gen AI, not only enhances customer engagement but also positions banks as lifestyle partners in the digital economy.
What’s more, AI models learn from the success and failure of past interactions to modify their own actions, reinforcing strategies and recommendations that invited interest, while dropping those that were met with disengagement. As it continuously learns and adapts, AI gets better and better at hyper-personalizing customer experiences at scale.
Moving forward in Europe
In Europe, next-generation providers are the clear leaders in AI adoption: Dutch neobank, Bunq, is Europe’s first AI-powered bank; Finn, its personal finance assistant, offers personalized, context-aware support through simple conversation, handling up to 40 percent of customer queries independently.
AI analyzes transaction data to offer personalized recommendations within Starling Bank’s mobile app, driving app engagement up by 25 percent. German digital bank, N26, personalizes customer experience through AI-powered multilingual support, conversational AI, and AI-generated next best action recommendations.
But overall, European financial service firms still have a long way to go. In a 2024 survey, although 90 percent of respondents said they had integrated some AI tools, just 9 percent believed they were ahead of the adoption curve. Investments are focused on back-office improvement, while AI adoption in customer-facing applications is clearly lagging.
Respondents named (lack of) understanding, experience and regulatory clarity as their biggest issues when integrating generative AI. Firms can address these concerns and accelerate implementations through training and development, by broadening AI use-cases, and by setting up strong governance and compliance mechanisms.
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