AI and machine learning have become a large part of the financial industry, and some within the industry are calling for banks to be more open to collaboration with AI startups to accelerate the implementation of AI in banking.
TD Bank Group’s group head of innovation, technology, and shared services, Michael Rhodes, who spoke on a panel related to these issues at Collision in Toronto, says TD’s willingness to work with AI startups has greatly accelerated its own use of the emerging technology.
“As banks have evolved over the years, we found that our data was quite distributed. And what artificial intelligence and machine learning bring to the table is the ability to aggregate that information so we can understand the context we are dealing with the customer,” indicated Rhodes in an exclusive interview with ITBusiness.ca. “So we can give them the right service, the right advice, the right content… in ways we couldn’t have five years ago.”
It was a need to be able to provide that type of service that led TD to acquire Toronto-based startup Layer 6 January 2018.
Layer 6 was founded in 2016 as a firm specializing in using machine learning models to analyze data to improve how individual customers are served.
Rhodes said that this acquisition greatly accelerated TD’s roadmap of implementing AI and machine learning in its practises.
“When we acquired Layer 6 we jump-started our ability to actually start deploying real use cases in a very significant way. And then also, it kind of forced an increase in the metabolic rate of how we took data, applied machine learning to it, and deployed it throughout the enterprise,” said Rhodes. “The models that Tomi and the Layer 6 team build are part of the story. And so the whole ecosystem is put together quite nicely.”
Co-founder of Layer 6 and now chief AI officer for TD, Tomi Poutanen, says the acquisition has not only sped up TD’s machine learning deployment but also allowed his company to take bigger risks in its innovative efforts; risks it would not have been able to take as an independent startup.
“For us, it was a decision on being able to do more strategic and more broad AI models and more ambitious AI work than we could have accomplished as an independent fintech service. If you’re outside knocking on the door, you may have success deploying one particular AI system. And as a startup, you look to then monetize that capability by selling it over and over and over again. The organization starts looking very much like a sales organization that’s good at sales and good at integration and good at security, but at the expense of investing in an AI challenge,” said Poutanen. “So as a set of data geeks, we really want to focus on building our AI expertise and our capability. And being inside of the bank, we’re able to do just that.”
It is the collaborative efforts between banking institutions and fintech startups that Rhodes says will help accelerate the use of AI in the financial industry as a whole; in a manner that is mutually beneficial to both sides.
“Fintech companies will identify generally a pretty narrow problem that needs to be solved around a specific customer segment. They’ll build some code and an application around that. But the fintech need a bank because we have a customer base, we have a regulatory umbrella, the funding and the balance sheet, and everything that goes with that. So we are always looking for opportunities to partner with fintech.”