AI adoption in risk management has reached a critical point. According to our new research, 65% of UK financial institutions have moved beyond exploration to active implementation. Yet 36% still have no AI models in risk, leaving them exposed as the competitive gap widens.
Early adopters are already seeing significant benefits, including enhanced credit risk predictions and fraud detection improvements. For those lagging, the data is clear: the cost of waiting now outweighs the cost of action.
In this short post, we’ll cover some of the research findings. 👇 If you want to dive into the full report, download it here now.
Barriers to AI adoption
Despite AI’s potential, organisations face challenges that go beyond technology. For instance, our research highlights governance, explainability, and validation frameworks as the most significant barriers, cited by 40% of respondents. These areas are critical to ensuring AI models are scalable, compliant, and effective.
Governance challenges are closely linked to explainability—ensuring that models provide clear, interpretable outputs that decision-makers can trust. Validation frameworks further enhance confidence by proving that AI models perform consistently and reliably, even under changing conditions.
Other barriers mentioned include:
- Staff capability gaps (27%): Many firms lack the technical expertise or in-house skills to implement and maintain advanced AI systems. Building these capabilities requires both targeted hiring and upskilling existing teams.
- Resource constraints (21%): AI projects often compete with other business priorities, leading to delayed implementation. Limited budgets, capacity issues, and fragmented processes can hinder progress.
If firms can overcome these issues, these are the practical use cases where AI has its biggest impact.
Where AI delivers results
The research identifies credit risk and fraud detection as the top priorities for AI adoption, each accounting for 39% of current AI applications. These areas are being targeted because they offer immediate, measurable impact, enabling organisations to improve decision-making and strengthen risk management in real time.
While organisations focus heavily on these areas, a significant opportunity remains untapped: 46% of respondents believe AI has the greatest potential to improve operational efficiency, yet only 11% have prioritised it. This disconnect highlights a gap between where AI is currently applied and where it could deliver transformative benefits.
Unlocking operational efficiency could streamline processes, automate workflows, and reduce manual intervention, allowing organisations to achieve faster and more accurate decision-making. For many firms, the potential for operational gains remains largely unexplored, even though it could drive significant improvements across functions.
As the research demonstrates, firms achieving the greatest success with AI adoption focus on building scalable governance frameworks, addressing skills gaps, and integrating AI into multiple business areas. These efforts enable them to not only enhance predictive accuracy in risk but also capitalise on AI’s broader potential. In fact, these results are what cause leaders to adopt AI.
Priorities for risk leaders in 2025
The report outlines three key priorities for organisations aiming to accelerate AI adoption:
#1. Build robust governance frameworks
Governance, explainability, and validation are often seen as obstacles to AI adoption, but they are essential enablers for success. Robust frameworks ensure that AI models are trusted, scalable, and aligned with regulatory expectations. Without these foundations, organisations risk undermining confidence in their AI systems, both internally and externally.
🔍40% of organisations cite governance as their biggest challenge, yet only 13% have established robust governance frameworks.
This gap presents a significant opportunity for firms to differentiate themselves by building systems that ensure compliance while supporting innovation.
Organisations succeeding in AI adoption prioritise model explainability, validation frameworks, and clear documentation protocols to meet regulatory requirements. These efforts not only mitigate risks but also accelerate the path to enterprise-wide AI adoption.
#2. Invest in skills and resources
Closing the skills gap is essential to achieve long-term AI success. Organisations must invest in both technical expertise and cross-functional collaboration.
🔍 27% of firms cite capability gaps as a primary barrier to adoption.
Organisations that invest in building internal capabilities report stronger outcomes in both AI performance and operational efficiency. By prioritising skills development alongside technology investment, firms can create a foundation for long-term success.
#3. Scale AI beyond pilots
Moving from isolated pilots to enterprise-wide integration is critical. Scaling AI across risk and operational functions amplifies its impact and positions organisations for future success.
🔍While 65% of firms are actively implementing AI, only 7% have achieved an advanced AI status.
Scaling AI across risk and operational functions amplifies its impact, creating opportunities for broader efficiencies and enhanced decision-making. Actually, those achieving enterprise-level integration are seeing measurable benefits, particularly in areas like credit risk and fraud detection, which account for 39% of current AI applications.
By extending these applications to operational functions, firms can unlock additional value, including faster processes, reduced manual intervention, and improved resource allocation.
Why now is the time to adopt AI
With 74% of firms at proof-of-concept stage or beyond, the competitive window for AI adoption is narrowing. Early adopters are setting new benchmarks in risk management, leaving others at risk of being left behind.
Our research makes one thing clear: AI adoption in risk has moved from competitive advantage to strategic necessity.
Keen to learn more? Download the full research report here. 📕 Or get in touch for an AI roadmap, tailored for your firm.
Further reading:
- AI vs traditional risk modelling: A comparative analysis
- AI risk modelling: Regulatory compliance and ethical considerations
- New guide: AI risk modelling maturity model
- Exploring Nationwide's ‘explainable’ AI-driven application scoring