Prepping for AI
For financial services firms that have held off dipping their toes into the artificial intelligence waters, it is not too late to commit, according to the experts.
“There is a tremendous opportunity for financial firms to use AI to reduce costs, improve efficiency, and improve the customer experience,” Mitesh Soni, senior director, innovation and fintech at Finastra, told IntelAlley during its recent New York users conference.
However, AI is not a panacea for banks, broker-dealers, or asset managers. Firms should not expect success if they plan to throw money at problems without having specific business improvements in mind.
“What tends to happen is a new technology becomes a buzzword, such as blockchain, and does not have a corresponding use case, and people try to shoehorn the technology into a business case, which does not work out,” he added. “
Once firms have particular use cases in their sights, they should evaluate which data they will use to train their AI engines.
“If you do not have your data in order, we are not going to have a good AI conversation,” said Steve Guggenheimer, corporate vice president, AI and ISV engagement at Microsoft and who spoke at the Finastra-users conference.
He noted that data quality had been the top issue of concern regarding AI with the Fortune 500 companies to whom he has spoken regularly for the past two years.
“If you are not getting insights from the data, let’s not try to add much intelligence to it,” he said.
When it comes time to decide to develop AI-based applications internally or rely on third-party offerings, both strongly suggest that firm look to the growing fintech market for the proper tools.
“Just because any financial services business on the planet can build a virtual agent to answer questions does not mean they should,” said Guggenheimer. ”People do it today because they can.”
For Soni, the choice is a matter of getting the biggest bang for a firm’s AI buck.
Rather than replicate the innovation of the 8,000 independent fintech firms in the US, firms should take advantage of the best offerings.
“Innovation happens at a massive pace, mainly through small startups that do not have any legacy technology,” he said. “They can implement their designs freely and fail fast. The consequence of a fintech failing is not as hight as a bank failing.”
The bank can access data science, artificial intelligence and machine learning for new products.
AiPEX with Watson simulates a team of analysts and traders to identify potential investments.
Machine learning models systematically scan newly arriving, anonymized data to identify anomalies.
The Cobalt programme launched in 2018 to help fintechs collaborate with the asset manager.
Users with different skill levels will be able to undertake machine learning and advanced analytics.