Alex Khomyakov: Hello and welcome to the Curiosity Code podcast. I'm your host, Alex. And today we're joined by Matt Gosden, a strategist and enterprise architect working at the forefront of pensions, wealth, and long-term savings. He advises organizations on how to apply AI and data in practical, responsible ways across highly regulated areas of financial services, from long-term savings to protection products and customer engagement.

His current venture explores how AI can help people make better financial decisions by understanding the complex rules and systems that shape their lives. Matt, it's great to have you on the show.

Matt Gosden: Yeah, lovely to be on the show. Thanks so much, Alex.

Alex Khomyakov: Let's dive into the world of long-term savings and wealth. What does meaningful innovation actually look like in that sector? Especially when you're working with decades-old infrastructure and deeply regulated environments.

Matt Gosden: Yeah. So I've been in the industry for a long time, maybe nearly 30 years now. I started off, my very first job was in tech actually, but I've spent most of my time in financial services. Financial services don't innovate as quickly as some other industries, especially tech, which innovates rapidly. This is partly due to regulation and legacy. We can talk more about that in a minute. But if you speak to most business leaders, meaningful innovation usually comes from thinking about how to improve the customer experience, particularly in savings and pensions, where engagement has been low for a long time.

Customers often aren't very engaged with what insurers or banks offer. Even with the rise of challenger banks, traditional banking can still be a bit sluggish, and certainly, pensions can be too. Meaningful innovation focuses on helping customers understand and use their products better. There's also a tech aspect, with discussions about platforms or re-platforming to deal with legacy issues. But for me, a lot of it is about the customer experience. The third area is the commercial levers. In industries like insurance, innovation around pricing strategies, customer retention, and cross-selling, often using tech, can also be quite meaningful and impact the bottom line.

Alex Khomyakov: Mm-hmm. So the infrastructure is quite old, right? My take on why it's old is because it works and there's not much interest in changing it since it's doing what it was designed to do. In the world of AI, we carry heavy technical debt from legacy systems that are still operational. They work, but as you said, innovation is driven by user experience and business opportunities. How do you navigate updating these core systems without disrupting the core trust, compliance, and continuity of a business?

Matt Gosden: Yeah, I can talk about the insurance and pension sides, but less about banking. These core systems require high resilience, security, and privacy, leading to cautious approaches with potential fragmentation over decades. Many financial services firms were early adopters of technology. In the late 90s, we still used mainframes for things like valuations, working with what, in hindsight, might seem dated but was necessary for scale.

The fragmentation arises because each company set up its own system, leading to complexity. We haven't seen one dominant platform taking over. There are platforms in the wealth space, but no universally accepted dominant player has emerged. This lack of a dominant standard means you're dealing with fragmented legacy systems. But not all legacy is bad; you can build service layers around it and modern APIs for integration, allowing for more contemporary functionality without discarding everything, despite slow innovation in this space.

Alex Khomyakov: So you mentioned a hypothetical provider that could overtake the industry. I'm surprised there's none currently. There must still be several key players in the market, right? Based on your experience, what are the biggest architectural challenges they face? Is it data unification as AI relies heavily on the state of data in the organization, user experience, or something else?

Matt Gosden: Yeah. There are some solutions, but no singular dominant platform like AWS or Azure. Salesforce is present in big insurers, but integration remains costly and complex. Using systems effectively is crucial and more expensive if not fully utilized. For AI, there's much talk about messy data needing modernization. Although data isn't neat compared to tech firms, you can still do scrappy work. Firms often focus on re-platforming and tidying data programs, but use case-driven approaches can be more effective. Starting with scrappy projects can prove concepts and build out data infrastructure once something works.

In AI initiatives, I wouldn't build a massive data infrastructure upfront. Start with specific use cases to see what you need, test with existing data, and iteratively productionize once something proves successful. Preemptively focusing on data can waste resources; it's often better to make quick moves with AI.

Alex Khomyakov: Everyone wants to move quickly with AI; it's a catch-up game. Talking about AI, you've written about the responsible rollout of AI in financial services. Where do you think GenAI can make the biggest impact in the pensions and wealth space?

Matt Gosden: Yeah, the pace in AI is incredible. Currently, many initiatives focus on internal-facing use cases to help knowledge workers become more efficient. This includes AI-assisted search across corporate data to improve document management. Internal-facing efforts work well as the human remains in the loop, acting as an assistant. However, we're exploring bigger opportunities in customer-facing applications.

Many customers now use tools like ChatGPT and Bard to ask financial questions online, receiving well-composed, mostly accurate responses. But these models' knowledge can run only 60-70% accuracy for complex questions and 80-90% for simpler ones. Consequently, some crucial decisions might derive from flawed information, posing opportunities and challenges for the industry.

The industry has an imperative to instill trust by ensuring advice via AI is sound. Encouraging customer engagement aligns with provider incentives. AI can simplify complex jargon and enable asynchronous communication with providers. While AI may never achieve 100% accuracy, well-structured customer-facing applications can outperform typical human interactions and improve the status quo in overall customer relations.

Alex Khomyakov: So your focus right now is on customer-facing solutions, right? As I picture it, I, as a user of the system, can ask to look at different solutions available in the market regarding my pension or long-term investment, analyze what's happening, and come back to advise something. It's more like an advisor, right?

Matt Gosden: Yeah, mostly. It's about enabling consumers to use services effectively. Let's take pensions. Many people tap into their pension pots without understanding tax implications or potential benefit losses. AI offers guidance, helping consumers grasp the implications of such financial actions without giving direct advice. This empowers customers to make informed decisions, aligning with providers' responsibility to improve consumer outcomes.

Alex Khomyakov: I work with AI daily, developing AI solutions and using them personally. With the fast-paced changes, it's like playing catch-up. Your industry is slow-moving and highly regulated, juxtaposed with the rapid AI landscape. Consumers also have high expectations from using AI solutions daily. How do you create a roadmap amid these dynamics?

Matt Gosden: Yeah, keeping up is challenging. In two and a half years, we've rebuilt our tech stack four times as capabilities improve. Competing with leading AI labs is tough since they have immense resources. Innovations mean constant reinvestment, which can be hard to manage.

The industry moves slowly because financial decision-making, particularly software buying, takes extensive deliberation, averaging 18 months for enterprise solutions. Herd mentality then kicks in; once someone adopts an innovation successfully, others follow. Our strategy involves getting something good live, sparking industry-wide progress.

Getting solutions into consumers' hands yields valuable insights. Early trials teach unexpected lessons, making the case for iterative, customer-facing solutions rather than perfectionism. Regulatory support, like the FCA's sandbox in the UK, promotes such testing, inviting firms to explore innovations safely.

Alex Khomyakov: Yeah, I can relate. In financial services, the process is slow but sticky, and the industry has immense capital. What unique innovation opportunities are overlooked in the pensions and wealth space?

Matt Gosden: I've discussed customer engagement: it's crucial. AI can enhance customer interaction with pensions and insurance, reducing jargon and frustration while encouraging engagement on their terms. Many already use chatbots for financial inquiries, indicating demand.

Additionally, strengthening technological backbones could catalyze further innovation. There's a need for standardized, open-source-inspired systems, driving commoditized solutions. Currently, semi-custom tech is expensive, with little differentiation. Streamlining tech bases could catalyze industry-wide innovations and foster ease of use.

Alex Khomyakov: If you had a blank sheet to redesign how this industry works, where would you start, and what would you leave behind?

Matt Gosden: The underlying products aren't complex—essentially being pots of money with options for withdrawal under varying tax and regulatory conditions. A standardized approach would simplify things. I envision a future where AI handles transactions similarly to financial advisors today, managing intricacies in the background and consulting users solely for critical decisions.

Such an agentic system could democratize professional advice, otherwise costly, generating outcomes driven by technical expertise. Although this is complex due to varying regulatory standards, it's an intriguing innovation. Distribution channels pose challenges, but this could enable better consumer experiences and financial outcomes.

Alex Khomyakov: This aligns with previous episodes discussing agents driving infrastructure development. I foresee AI agents assisting with daily finances across many agent-driven APIs. Modernizing infrastructure towards API-centric designs makes it agent-friendly, rather than relying on layers built for manual data handling.

Matt Gosden: I agree. Creating agent-friendly APIs isn't difficult, merely enabling enriched inputs and outputs for more engaging interactions. For instance, retirement calculators could benefit from richer outputs beyond simple numerical results, allowing deeper understanding. Developing agent-friendly services makes interactions meaningful for both agents and users. In reality, companies must integrate these agents while enhancing internal services. Thank you.

Alex Khomyakov: Well, I'd like to wrap this up and thank you for your time. I enjoyed the conversation a lot. Thanks a lot, Matt.

Matt Gosden: No, thanks very much for inviting me along. It's been great.

Alex Khomyakov: And to our listeners, as usual, thanks for listening to the end. Don't forget to hit the like button on YouTube, subscribe to the channel, and if you're listening on any podcast platform, please leave a positive review. See you in the next episode. Bye-bye.