The allure of AI for efficiency, profitability, and reimagined customer experiences is drawing global attention. The challenge for European banks isn’t a lack of interest, but the path to execution. The reality for many banks is a confusing mix of pilot projects and unfulfilled promises. Success in leveraging AI and GenAI is increasingly tied to how well financial institutions can leverage data. A simple framework can help here.
Few topics arguably dominate conversations in industries as much as artificial intelligence (AI) and generative AI (GenAI) do today.
If Google Search Trends are any indication, the worldwide interest in GenAI shows little signs of abating. The values in the graph, by Google's own definition, represent search interest relative to the highest point on the chart for a given region and time; GenAI is maxed out at 100—the peak popularity for the term.
Fundamental questions and boardroom imperatives
The banking industry is no exception. AI and GenAI feature prominently in boardroom agendas, alongside other items, as leaders explore ways in which advancements in these technologies can help them improve productivity, deliver efficiency gains, and create new value. Management consulting firm McKinsey estimates that with the emergence of GenAI, productivity could improve by 3% to 5% and reduce operating expenditures of banks globally by up to $200-$300 billion.
The research and consulting firm Gartner placed GenAI at the peak of inflated expectations in its 2023 hype cycle for emerging technologies. Gartner uses this as a methodology to describe the phases of maturity and adoption of a technology to solving real-life business problems and leveraging new opportunities.
Not to be left behind in the AI journey: The European banking landscape
Materialising business benefits requires investments, and on that front, there are signs that European banks and financial institutions (FIs) are resolute.
In an EY survey of executives from European financial institutions, 60% said they had allocated capital to GenAI technologies in 2023; 75% said they planned to increase spending in 2024.
The banking industry, though, is not new to AI. It has been an early adopter of machine learning models to automate routine tasks and predict market behaviour, among other things.
Many banks are experimenting with AI and GenAI in areas where they believe it can augment existing processes and deliver greater business results.
Barclays, for example, uses AI to monitor merchant payment transactions in real-time to predict potential fraud. Banco Santander's AI tool, Kairos, enables the bank’s employees to make more informed investment and lending decisions by showing the predictive impact of economic events on its corporate clients.
On the regulatory front, the European Union (EU) also passed the EU AI Act, outlining a framework for developing and using AI technology. However, European regulators recognise the potential of AI and do not want to be left behind. In March 2024, the European Central Bank (ECB) announced that it found 40+ use cases for GenAI in banking supervision and developed 14 AI applications and platforms.
Around the world too, there is progress. Bank of America's Glass uses the models and machine learning techniques it has built for its sales and trading employees to discover unseen market patterns and anticipate client needs by consolidating market data across asset classes and regions. Discover Financial Services is using GenAI to improve customer experience by empowering its call centre agents to understand their customers better and resolve queries faster.
Ears on the ground: What banks want to do
While the implementations of AI and GenAI in banking remain an evolving landscape, there are several business opportunities where AI can provide a significant strategic advantage for banks. For example, in our conversations with leading banks, they expressed that they would want to leverage AI to provide value-added services such as cash flow forecasting to their corporate and business clients—a sound use case considering how such a service can be an added source of revenue for banks and enhance the experience for their clients. We will discuss this use case in this article – there are multiple use cases across banking segments, functions and channels.
Getting the data fundamentals right
While cash flow forecasting is one of many use cases, the fundamentals of leveraging AI and GenAI lie with building the right data framework to unlock their full potential.
Banks and FIs sit on a goldmine of customer data. Like a library filled with priceless knowledge in countless unread books, valuable insights lie buried within bank data, waiting to be explored.
While many FIs have embarked on data-driven initiatives, scaling those projects to achieve strategic advantage remains challenging. McKinsey estimates that only 15% of businesses' machine-learning projects ever succeed; Gartner found that just more than half—53%—of AI projects ever make it past the prototype stage to production.
There are many reasons for this.
First are siloed initiatives: data projects often exist in silos, failing to leverage the interconnectedness of information across departments. This isolation creates redundancy, hinders collaboration, and limits impact.
Then you have a talent gap. The demand for skilled data scientists and engineers outstrips supply, leaving many FIs struggling to build and maintain in-house capabilities. European banks also carry the burden of legacy systems; integrating new data solutions with existing Information Technology (IT) infrastructure can be complex and costly, creating integration bottlenecks and hampering agility.
Last is ensuring data privacy, security, and compliance in a rapidly evolving, stringent European regulatory environment—a crucial step but complex to navigate.
Conclusion
In conclusion, while the potential of AI and GenAI in transforming European banks is undeniable, the journey to becoming disruptors rather than the disrupted hinges on more than just technology. It requires an integrated approach to data strategy, cross-functional collaboration, talent acquisition, and navigating regulatory complexities. Banks must move beyond isolated experiments and pilot projects to scale AI initiatives that drive real business outcomes. By addressing legacy infrastructure challenges and investing in a robust data foundation, European banks can fully harness AI's capabilities, creating new revenue streams, enhancing customer experiences, and leading the charge in financial innovation on the global stage. The future belongs to those institutions that are not just quick to adopt AI but also smart in executing it at scale.
