Two-thirds of UK banks are struggling to implement AI on outdated core systems, likening the situation to trying to fuel an electric vehicle (EV) with petrol. As a result, more agile fintech companies are outpacing traditional banks in AI adoption, according to new research from SaaScada.
The latest report, AI in Banking: Big Ambitions, Broken Foundations, highlights the significant hurdles faced by UK banks, including poor quality data, reliance on legacy technology, and ongoing regulatory uncertainty. Despite the promise of AI, the research reveals that adoption within UK banks remains sluggish, with fewer than half of institutions having deployed AI across their operations.
SaaScada surveyed 150 innovation leaders within UK banks and found that although 80% believe the sector is ready to fully embrace AI, and 81% expect it to have a major impact on the industry, only 55% of banks have actually integrated it into their services. This slow pace contrasts with the wider financial sector, where 75% of firms have already adopted the technology, according to data from the Bank of England.
Consumer behaviour further highlights the gap, with recent research from Lloyds revealing that 28 million adults in the UK now use AI for personal finance, and more than half have relied on it for financial advice in the past year.
What’s Holding Banks Back?
SaaScada identifies poor data quality and lack of real-time insights as key barriers to AI adoption. Nearly two-thirds (63%) of banking leaders believe that AI in finance is “going nowhere fast” without access to accurate transactional data. Moreover, 79% of respondents agree that a solid data foundation is crucial to keeping up with AI-driven innovation.
Additionally, many UK banks continue to struggle with outdated core systems. The study found that 66% of banks are attempting (and often failing) to implement AI on these legacy platforms, which has been likened to trying to fuel an electric vehicle with petrol. As a result, 79% of UK banks report that more agile fintech challengers are pulling ahead, leaving traditional institutions scrambling to catch up.
Regulatory Uncertainty and Compliance Concerns
Even if banks manage to address these technological challenges, many are still waiting for regulatory clarity on the future scope and demands for AI use in finance. Over 60% (63%) of banking IT leaders say that the prospect of more compliance and reporting requirements has made them hesitant to adopt AI. Similarly, 68% cite regulatory uncertainty as a significant barrier, causing them to push their AI initiatives to the back burner.
However, most banks are not seeking less regulation but want more clarity and support from regulators. More than half (54%) agree that the Financial Conduct Authority’s (FCA) cautious approach to AI will help address risks such as bias, inaccuracy, and data privacy. Additionally, 67% believe that while stricter regulation will likely slow adoption, it’s a necessary step to ensure proper oversight and prevent misuse.
Nelson Wootton, co-founder and CEO of SaaScada, remarked, “The FCA isn’t going to reinvent the rulebook for AI, and nor should it. It’ll fold AI into existing principles and judge firms on outcomes. That’s the right approach.”
Moving Forward
Wootton added that banks are missing the point if they continue to wait for new rules. “The guardrails already exist, and waiting for new rules is just an excuse not to act. Banks burying their heads in the sand are missing the point – AI won’t just need compliance; it’ll assist with compliance. But only if they get their act together on data.”
As UK banks continue to lag behind in AI adoption, the need to address foundational data issues and regulatory concerns is becoming ever more urgent. Only by overcoming these challenges will banks be able to fully leverage the potential of AI in transforming financial services.