Chris Gledhill, CEO of Secco Aura and frequently referenced as one of the top 10 fintech influencers, speaks to bobsguide about going beyond 'incremental innovation' towards genuine, irreversible disruption.
How did you get into banking?
I started off on the tech side with a degree in computer science from university, and went down the programming route early in my career. I tried my hand in all sorts of sectors like defence, retail and the financial services which gave me a great overview of the different parts of the lifecycle in different projects. I eventually became a lead mobile architect for Lloyd’s Banking Group, where I developed their next generation of mobile apps and that was right at the start of the fintech boom five years or so ago. From there I moved into their innovation team where they essentially bounced business-led innovation ideas off my tech sounding board.
I found that I tended to gravitate towards innovation roles and eventually launched ‘Disruptive Innovation labs’ within Lloyd’s Group. The aim here wasn’t just to incrementally innovate, i.e. quicker, cheaper, better, but it was more about looking at horizon-free concepts and looking five to ten years down the line.
Why did you leave conventional banking for the fintech sector?
I left Lloyd’s because I thought that maybe it would be easier to innovate from the outside-in as opposed to inside out; it’s quite hard to push that type of innovation in a 250-year-old organization. Whilst at Lloyd’s I was given the opportunity to visit Silicon Valley and that’s where I took an interest in the power of social media, particularly Twitter and the idea of social influencing. With my own startup, Secco, I soon found that, as my social influence grew, banks and investors were coming to me and less frequently I was pitching to them. My social influence also gave me access to a lot of conferences, and the ears of people far smarter than me, and that really helped grow the brand and business of Secco.
Let’s separate the wheat from the chaff with the emerging technological trends of AI, blockchain and big data.
From a fintech influencer perspective, we always have this conundrum where we understand and can see through hype to the practical uses of the more sustainable tech trends. However, at the same time we have people asking us to speak on blockchain to a 100 blockchain fanboys and adding to the hype. No one will pay you to stand on stage and say that bitcoin is a bubble and a bit rubbish because you’ll get stoned to death. It’s interesting trying to strike the balance between being genuine and realistic about tech hype.
Generally, to separate the chaff from the wheat, you have to strip the buzzwords back to their secondary levels. AI, for instance, is such an overarching term that you have to unpack it into the different sectors like neural networks, robotic process, automation, machine learning – to see what sort of impact it will have. From there, you can branch off in a tree paradigm to see whether we’ll simply get incremental innovation or we’ll totally spin off into something new and unforeseen. The second way of cutting through the hype is not to underestimate how long these processes take. In five years, we all think we’ll have a personal robot and that’s something we’ve been saying since 1960!
A good example for the financial services is virtual reality. Banks tend to simply apply new tech to the existing world; for instance, creating a virtual reality banking experience where the customer puts on their headset and can meet their mortgage advisor in a virtual branch. To be truly innovative and to put that technology to a wholly new use within mortgages, you could put on your headset and see a virtual heat map of viable properties with the best scores and crime rates in London whilst also visualising your mortgage down payments in 3D visuals. You could even go one step further, and set about solving the problem that will arise from it down the line, virtual reality identity authentication; the truly innovative banks will become identity brokers.
These are the sort of things I try and explain to banks in some of my private gigs in fancy restaurants where I explain to 10 or 12 executives which technologies they should really be investing in. In effect walking them through whether they should be doing some proof of concepts to tick the blockchain box, or going all in with £100m investments.
How would you recommend cutting through the hype?
It boils down to being pragmatic about the tech. Of course, everyone needs a token blockchain guy, who understands it and can monitor it. Take Blockbuster 20 years ago. They had a guy who was messing around with streaming and at a certain point, that guy had to put his hand up and tell the board to begin taking it seriously. At the moment, things like blockchain aren’t being taken seriously by banks.
The Cloud is a great example. In 2007, the big consultancies generated lots of hype around the Cloud and SaaS and how it would transform business operations and reduce IT costs, but it disrupted in a wholly unpredictable way. 15 years on, the big banks are beginning to implement some services in the Cloud, but the initial Cloud providers catered to startups looking to lease server time; that’s where the disruption has occurred.
That’s the problem with emerging tech, we apply it to the existing business landscape and how it might predictably disrupt. For whatever reason we don’t think about the unknowns-unknowns, chaos-style disruptions that may arise from the technology. It pulls back to my point about going beyond incremental innovation. At Secco, we’re very conscious about using AI and blockchain and applying it to wholly new concepts.
Which technology are you most excited about?
Machine learning, particularly neural networks. The scariest thing about the neural networks side of machine learning is that we don’t really know how it works. We basically make a matrix of neurons, input data, and get it to make a decision until we believe it has consistent decision making. But we don’t have the audit trails so we don’t know how itt works properly.
This could be applied in the financial sector to risk matrices or credit scoring which makes it very interesting but also very scary. For instance, you could take historical data of repaid and defaulted loans. If you ask the machine, based on this information, whether the next person will repay or default and how they came to that decision the answer is probably not one you want to hear, because after all, you’re asking the machine to discriminate, often by group, and answers can be racist. You’d need some way of implementing a regulatory framework or moral code to ensure the machine doesn’t discriminate unfairly. That’s the difficulty from the regulatory perspective, getting the decision audit trail. It’s nearly impossible to get a neural network AI to fully justify why it denied someone a loan. The answer is to incorporate consequence into the process by adding carrots and sticks to discourage and incentivise behaviour. By inputting negative feedback from a human user, it will programmably learn not to do something.
To be clear, in the short term neural network AI will help with risk profiling, credit scoring and trading and not the more popular notions of AI robots walking about in branches.
Read Pt. 2 here. Chris dives deeper into how neural networks will completely change banking.