Hello everybody and welcome back to C&F Talks. Today, it's my great pleasure to have with me Chris Huggett, who's the Chief Commercial Officer at Aker Systems. Chris's colleague, Tim Cassidy, who's the CTO there, will be speaking at our forthcoming Data, AI and the Future of Financial Services Summit on the 16th of March in London.

In terms of Chris's background, during a 35-year career in the technology industry, he has held senior European, German and UK leadership roles in sales, marketing and general management. He's successfully led teams at large technology vendors such as Cisco Systems, Hewlett Packard, Dell and SunGard Availability Services. Chris, welcome today.

Thank you, Maurice.

It's great to have you with us.

So, let's turn to our first question, and it's really one by way of background. For those who aren't familiar with Aker Systems, how do you describe what you do and the value you provide to your customers?

Yes, well, first of all, good morning. Hello, Maurice. Very nice for us to be here.

Now, we are a UK-based supplier of technology services and solutions to UK industry and government. We describe ourselves as the data modernization company. What that means is we design, build, operate advanced AI-ready data infrastructure and solutions that help organizations become truly data-driven with speed and certainty.

Now, we do that in some of the most complex environments that exist. We specialize in mission-critical, highly regulated environments. For example, many of our engagements are dealing with critical national infrastructure.

So, we work where data needs to be kept secure and where data needs to be trusted, and in many ways, above all, where there's a real-time or near real-time requirement in a customer's data challenge. So, our focus at a business level is helping organizations make better decisions, move faster, and, for example, adopt AI with confidence. So, we think what makes us credible to financial services businesses is the scale, the complexity, and that criticality of the problems we solve.

They might be customer environments where resilience or security or governance, operational certainty matter as much as innovation, where we can enable organizations, as I said, to become truly data-driven with that speed and certainty but without increasing operational or regulatory risk.

Maurice

So, as you say, financial services is a key sector for you, and I suppose it provides its own unique challenges. What are those in this particular sector?

Well, when it comes to data and AI and the sort of things that need to be in place for AI, for example, to scale safely, this is obviously a very important question as three-quarters of British financial firms now do use AI across core functions, not around the edges, but across core functions from things like processing insurance claims to credit assessments.

So, those financial services organizations are facing a broad and compounding set of challenges around data and AI rather than a single issue to solve. So, for example, legacy fragmented estates when it comes to data, that makes it difficult to meet the rising regulatory expectations around evidence, lineage, accountability, security, call it what you will, particularly as AI is introduced into decision-making.

So, siloed and inconsistent data will hamper insight generation, will hamper reuse, and that's foundational for trust in that data. And at the same time, of course, there is a growing pressure to deploy AI in not just around the edges, as I say, but in high-impact use cases, things like fraud detection, things like underwriting, claims automation, customer experience, particularly in insurance. So, those use cases are understood, they're proven, but scaling them safely is hard, and they depend on combining structured and unstructured data across multiple systems with clear ownership and traceability and control.

So, if you lack strong data foundations, then AI tends to work in isolated pockets, which isn't really the desired outcome. It's impressive in demos, yes, but it's difficult to govern, and it's difficult to defend at board and certainly at regulator level, if that's the approach. So, that's certainly one challenge.

Now, another challenge we see is increasingly important data sovereignty and resilience. Clearly, we live in a more volatile geopolitical environment. Organizations need confidence over where their data resides, who controls it, who has access to it, and how resilient their platforms are to disruption.

And we've all seen the examples in the public domain of organizations whose resilience has proven to be inadequate at critical moments, and the disruption has caused to their business and to their reputations. So, for AI to scale safely in FSI, we believe, and you expect us to say this, of course, that the data architectures inside those organizations have got to support data security, they've got to support governance, they've got to support that operational resilience, and the need to be sovereign by design, not retrofitted after the fact. So, for us, the data architecture is key.

Yeah, I can understand that's a problem. Having worked in financial services myself, the number of legacy systems which are cobbled together through a series of acquisitions could make that quite challenging, I should imagine.

But turning to some of the technologies, what are the technologies that are shaping the direction of the sector, capitalizing on AI and other innovations?

Well, the big technology shifts in financial services are all converging on one thing, and that is trusted, automated decision-making at scale. So, you've heard the term agentic AI, that's moving from experimentation to operations, AIs that actually make decisions as opposed to just produce content. So, in that field, if you're moving from experimentation to actually operational deployment, the differentiator there is going to be something we call bounded autonomy.

So, clear permissions, guardrails, escalation paths, auditable actions to manage how an AI agent actually gets to make decisions and give operational value. So, we think that's probably the first one. Secondly, decision management around that is becoming the control plane.

So, you're combining models with rules and workflows. So, the outcomes are explainable, testable and governable, and that helps you support and justify an AI deployment. And third, all of this depends on real-time event-driven data platforms.

The AI is only as good as the data that supports it, and we see so many times people kick off AI initiatives, and the first thing they discover is that their data foundation is lacking, and then that radically reduces the effectiveness of that initiative. So, your real-time event-driven data platform will help you with things like fraud, claims, underwriting, customer journeys, running on live signals, not running on yesterday's data, running on live signals. And around that core, we would say FS leaders are investing in AI lifecycle governance, automated evidence by design, things like lineage, policies, code, audit packs, and clearly with security, and this bit's not new, security, resilience, sovereignty baked into those platforms.

And that's where Aker’s heritage translates directly here. We build mission-critical, secure, real-time data foundations. We're not an AI company, but we build those foundations so that a regulated firm can adopt these innovations with confidence.

Understood.

And looking to the future, say looking ahead to 2030, what are the biggest strategic opportunities in FS, especially for insurance? Because a lot of what you said, you've made specific reference to the insurance sector.

Yes, well, it's clear from our conversations with insurance companies that a lot of them are saying the same thing, which is that data becomes the differentiator. Data becomes a strategic asset to organizations. So, the biggest opportunity thinking forward on that sort of timescale is trusted interoperability, sharing the right data with the right controls across an ecosystem at the speed of operations.

So, insurance will increasingly be real-time. It'll be contextual, it'll be connected. So, you've got faster claims journeys from a customer.

You've got proactive risk prevention. You've got more dynamic pricing, and you've certainly got better fraud detection through those connected signals. But that only works if you can share data securely and prove that there's consent, there's provenance, there's accountability and that you're dealing with, as we said, real-time data, not yesterday's.

So that's where I think Aker’s public sector heritage, and we've worked a lot with central government over the years. We've built national data sharing platforms for large central government clients where multiple organizations collaborate without sacrificing control, without sacrificing trust.

So, I would say in summary, the future isn't just more AI. There are some who might give that answer, but it's not just more AI, although that clearly is coming. It's more trusted data movement across more parties with stronger evidence and governance.

And data really is, from everything you've said, is the foundation of AI. If you don't get the data right, you can't have a decent AI system. Would that be a fair summary?

That's exactly how we see it, yes. I mean, clearly the trend of AI adoption into more and more of organizations' core application environments and core operations is unstoppable and largely to be welcomed, but there is always, and it sounds like the boring bit, it sounds like the people selling spades in the gold rush, that underlying data foundation is really key.

Absolutely. Well, for our viewers, if you'd like to hear more about this, I suppose the clue is in the title, but the summit is called Data, AI, and the Future of Financial Services. The event is on the 16th of March. It's being held in central London. 

Further information is available on our website, www.cityandfinancial.com. So, if you'd like to see further details and book a place, please do visit the website.

It only remains for me to say thank you very much, Chris, for sharing those thoughts with us today.

Thank you, Maurice.