Under Edinburgh's bright skies at Dynamic Earth, the inaugural DIGIT Financial Services Technology Summit convened a strong cohort of professionals from across banking, asset and wealth management, pensions, insurance, and fintech. The event fostered engaging discussions around the most pressing trends and challenges in the sector, set in a venue perfect for networking and  insightful conversations.

The financial services landscape has long been shaped by technological advancements, enhancing customer experiences and operational efficiencies. And now, we're entering a whole new era with advancements in digital tech, AI, open finance, and the potential of quantum computing. But, the industry is still dealing with ongoing issues like organisational structure, data problems, and outdated tech. Against this backdrop of both persistent challenges and groundbreaking innovations, the financial services sector finds itself at a fascinating crossroads. 

Our team was on the ground, deeply involved in these vital discussions, gathering key insights from industry leaders and getting a first-hand look at the issues delegates are tackling.

In the first of this two-part series, we’ll be diving into the initial three pivotal themes that emerged from the summit.

1. AI: From hype to business value

Artificial intelligence is rapidly transitioning from a futuristic concept to a tangible reality within financial services. The summit highlighted the current maturity of AI, its diverse innovation opportunities, and the emerging cybersecurity risks it presents.

Alex Radu (JPMorgan) illustrated how AI is boosting productivity and accelerating innovation throughout their product development lifecycle. He emphasised AI's power in pre-build stages for rapid customer research, ideation, and prototyping, leading to a greater variety of product concepts at unprecedented speed. In build and test phases, AI acts as a valuable co-pilot for engineers and testers, enhancing productivity through diverse solution exploration, automated code generation, and test automation. Post-launch, AI facilitates continuous monitoring and feedback, shortening iteration loops and enabling dynamic scaling. Personalised content, experiences, and predictive customer support were also noted as key AI capabilities.

However, several speakers cautioned against unbridled enthusiasm. Radu stressed the importance of a robust adoption framework to capture value, manage risks, and build trust in AI among engineering teams, alongside a willingness to learn from failures. Andy McMahon (Barclays) underscored the necessity of aligning technology, business, and customer needs to effectively leverage AI. He presented a framework of Generative AI use case patterns (search, summarise, analyse, generate, translate/transcribe) to help organisations validate whether a specific use case truly necessitates generative AI. McMahon also cited research indicating that enterprise GenAI adoption is still in early stages, with many firms in pilot phases.

Mari Parry (BlackRock) echoed the need to ground AI investments in sound business principles, advocating for a structured approach encompassing opportunity identification, clear ROI metrics, and rigorous measurement. Stephen Bosarge (Dell) offered a forward-looking perspective on agentic AI, predicting significant adoption and cycle time improvements in the near future.

Key takeaway: While AI offers significant benefits in productivity and time-to-market across the product lifecycle, its implementation requires alignment with robust business cases and careful control through adoption and risk management frameworks.

2. Data: The bedrock of innovation

Discussions on AI invariably overlapped with the crucial role of data. Speakers consistently emphasised that robust data management and governance are not merely supportive functions but fundamental prerequisites for realising AI's true potential.

McMahon (Barclays) asserted that a strong data foundation is both essential for AI success and a key differentiator, arguing that while AI technologies become more accessible, proprietary data and the ability to leverage it effectively will provide a competitive advantage. He suggested organising around platform teams managing core infrastructure, data, AI, and engineering tooling (XOps) to support agile use case teams.

Dave Townsend (LBG) echoed this, highlighting data's central role in LBG's transformation and the need to overcome "data islands" and inconsistencies. Shauna Mullin (M&G) shared a similar experience, noting how data silos hindered their ability to deliver personalised customer interactions, a challenge addressed during their AI proof-of-value initiative.

Key takeaway: In the age of AI, data transforms from a byproduct to a strategic asset. Competitive advantage will increasingly depend on financial institutions' ability to effectively manage, govern, and extract value from their proprietary data.

3. Bridging the advice gap with AI

The "advice gap" in wealth and pensions, marked by low customer engagement, was a significant topic. Derek Smith (Aberdeen Advisor) presented statistics highlighting the limited reach of financial advice and the operational challenges faced by IFAs.

Smith argued that AI holds the potential to bridge this gap by enhancing both customer engagement and the efficiency of advice delivery. However, he stressed the need for improved data access, integration, and system modernisation. AI can act as a powerful co-pilot for advisors, automating tasks like fact-finding, data capture, CRM integration, and report generation, enabling them to serve more clients and focus on high-value interactions. Furthermore, proven AI tools could be deployed directly to customers, expanding access to personalised financial guidance. AI can also facilitate intelligent customer segmentation for more targeted advice services.

Mullin (M&G) detailed their 12-week initiative focused on reducing the time required to provide advice, streamlining complex systems, and meeting the growing customer demand for personalised and affordable services through a framework to rethink client relationship management and a co-pilot AI solution for advisors.

With the upcoming advice-guidance boundary review and increasing significance of intergenerational wealth transfer, leveraging technology effectively will be crucial in the wealth and pensions sector.

We dive deeper into this particular issue in our recent whitepaper - ‘Mind the Gap: Bridging the UK's pension divide with intelligent digital solutions powered by AI’ - which you can download here.

Key takeaway: In the short to medium term, AI's primary role in closing the advice gap lies in enhancing advisor effectiveness and scalability by automating administrative tasks and providing intelligent support.

Looking ahead

The DIGIT Financial Services Technology Summit provided valuable insights into the key technological trends and challenges shaping the future of the industry. From AI-driven innovation to the imperatives of data security the discussions underscored the necessity of strategic vision and agile execution.

CreateFuture stands as a trusted partner to financial services institutions across the UK, supporting the industry in navigating these evolving landscapes, balancing innovation with modernisation, and delivering exceptional value to businesses, colleagues, and customers. Get in touch with our expert team to learn more.

Thanks again to DIGIT and the exceptional speakers for a truly insightful and engaging day. Stay tuned for part two, with even more key insights from the day, covering how cybersecurity, digital transformation and open finance are shaping the industry.