Companies in fields such as financial services and insurance live and die by their data—specifically, how well they can use it to understand what people and businesses will do next, a process increasingly dominated by artificial intelligence . Now a startup called Finbourne, based out of London’s financial hub, has built a platform to help financial companies organize and use more of their data in AI and other models. It is announcing £55 million ($70 million) in funding, which it will use to expand its reach beyond the Square Mile.
Highland Europe and AXA Venture Partners (also known as AVP and backed by the insurance giant of the same name) are co-leading a Series B round that values the company at just over £280 million ($356 million) after cash-out.
Thomas McHugh, the CEO who co-founded Finbourne, told TechCrunch that he came up with the idea for the startup after many years working in the City as a senior quant, most of those spent at the Royal Bank of Scotland. One of those years was 2008, the year RBS, then the world’s largest bank, came dramatically to the brink of collapse after being overexposed to a contagion of subprime loans.
The main shift took place internally in the form of a huge reorganization.
Previously, the entire bank was organized into a number of business forces, which affected not only how people worked, but also how the data within them worked. All this cost a fortune, a cost that urgently needed to be cut. “We had to cut hundreds of millions of costs out of the business in a very short period of time,” he recalled.
They decided to take a page from the nascent but fast-growing world of cloud services. Founded in 2006, AWS had only been operating for two years at this point, but the data teams saw it as a compelling and benchmarking model for how a bank could store and use data. So it too took a consolidated and federated approach to the problem.
“We’ve basically been able to build an awful lot of technology that has worked across all asset classes. Until then, people said it really wasn’t possible. But we had an incredible reason to change, and from that we knew we could build a better technology, a much more scalable technology,” McHugh said. Equity schemes, fixed income and credit, he said, all previously operated as separate schemes, were now on one platform.
The UK financial crisis of 2008 was like a rollercoaster, if you weren’t completely thrown off you would certainly leave believing that you could stand up to any challenge. So, of course, this ultimately led to McHugh taking on the riskiest of all things in business: a startup.
Finbourne may have its roots in how McHugh and others on his team took on the challenge of building more efficient data services at their bank, but he also developed an idea that reflects and shapes the way financial services companies buy IT today. Just as companies with large sales operations may use Salesforce or a competing platform rather than build their own software, Finbourne is betting that financial companies will increasingly do the same: work with external companies on tools to manage their operations instead of building their own. .
Inevitably, this also fits into how banks and other entities in financial services are increasingly working with artificial intelligence.
The company’s products today include LUSID operational data storage; investment and accounting ledgers (used in asset management analysis); a portfolio management platform that tracks positions, cash, P&L and exposure; and a data virtualization tool. McHugh said Finbourne is also helping to manage how companies handle data for training models, an area where he is likely to become more involved.
The main takeaways seem to be that there’s no clear leader and banks don’t want to share data with other banks, so they’re training how to avoid it – a process that also helps customers keep a tighter check on results and prevent them from creeping into the picture “hallucination”. Open source plays an important role in how it presents more flexible options to end users.
“What we’ve seen is that customers don’t want to train any of the models we write or use on someone else’s data,” he said. “We see it very strongly. We do this because by not being allowed to use someone else’s images, these models are less able to hallucinate.”
Finbourne currently has a number of competitors. Asset management competitors include Blackrock’s Aladdin, SimCorp, State Street Alpha and Goldensource; Alternative asset manager competitors include Broadridge, Enfusion, SS&C Eze and Maia. BNY Mellon Eagle, Rimes, Clearwater Analytics and IHS Markit offer tools for asset owners; and service assets include FIS, Temenos, Denodo, SS&C Advent and NeoXam.
The fact that there are so many may be one compelling reason why someone would take the simpler approach and work with just one – the route taken by companies such as Fidelity International, the London Stock Exchange Group, Baillie Gifford, Northern Trust and the Pension Insurance Corporation ( PIC) take.