Fintech
How to decide whether to build or buy fintech, AI technology
The decision whether to build your own technology platform or program rather than through a partnership is becoming more complex and often more expensive as technology advances in machine learning and artificial intelligence.
Any learning model requires more data and updates to work better, which also raises concerns about the security of data streams and whether to develop the technology in-house or work with a technology partner to streamline that process.
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“That’s the multimillion-dollar question: Build versus buy,” said John Mackowiak, chief revenue officer at Advyzon, a cloud-based portfolio management platform based in Chicago.
Mackowiak said his company builds technologies internally primarily to create a consistent user experience, but also because adding vendors would require integrations into different systems, which can be complicated.
“Certainly [building in-house] It wasn’t the easiest road to take. But over the ten years since we launched, it’s been a game-changer,” he said. “Because with the idea of integrating one vendor with another, you end up with a fragmented technology stack and user experience fragmented.”
But building new technologies in-house isn’t always the right fit for everyone. Every company has a different business strategy, budget, customer base and a different level of technology development capability.
“You have to understand your use case really, really well. What are the economics behind it? And then, AI, just like any other technology, is its capital,” said Lee Davidson, head of data and Morningstar analysis. “Usually, you have capital and labor to deploy to solve a problem. And you have to find the right mix to make the production function work and meet the customer’s needs.”
Davidson provided an example of learning from “failures” when he began experimenting with machine learning models in 2011 to generate investor insights for users.
“They never went anywhere” because “as a researcher working on this, I thought people cared about accuracy,” he said. “They care about transparency.”
Morningstar decided to use machine learning and large AI-powered language models to better explain to users how a response was formulated.
“What we found is that there’s this trust factor, this explainability, so we started to package it into more explainable pieces,” he said. So the user tool “talked about the bottom, talked about the context… and we got a lot more usage because people understood that there was some evidence behind it, some context.”
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Morningstar also implemented new tools across large language models, such as ChatGPT, to help with the launch Artificial Intelligence Research Assistant Mo last year. Increasingly, a company might build a system in-house but outsource another feature or tool to a more specialized technology company.
“It’s hyper-individual. For us, we have our own internal team that designs what we’re doing from an AI perspective. But we use third-party tools to manage the system,” said Chris Shuba, CEO of Helios, a quantitative asset management platform that uses learning technology to perform portfolio data analysis on thousands of mutual funds and ETFs. “Some people don’t do it at all. They just tell an AI developer, ‘Here are the results I’m looking for. Go do it and we’ll pay you for the project.’ Other people might have some hybrids.”
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For example, Envestnet is a large Wealthtech provider offering in-house built technologies, but has also partnered with specialist technology providers such as iCapital to offer an alternatives exchange on its platform for client advisors.
Dana D’Auria, president of Envestnet’s solutions group and co-chief information officer, said the company chose to work with iCapital and others that were “established vendors” specific to the alternatives sector.
“It’s not something we’re going to build internally because No. 1, that would be a huge, distracting task,” he said. “And No. 2, you already have leaders in the industry who are part of the same customer ecosystem that we serve. So it’s an obvious one to partner with.”
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D’Auria said that when considering whether or not to build in-house, companies should first evaluate what the build will look like, whether bringing resources in-house would distract from overall goals, or whether internal staff have a better understanding of the business and integration .
“Bringing a build in-house takes resources, but at the same time… you have the same team working on proposals, the same team working in the billing environment – all those same people are now building it directly into the solution.”