This article in American Banker looks at all the ways Bank of America is looking to leverage machine learning and AI, but also indicates that the bank is well aware of the many different vectors in which bias can enter AI, from biased training data to inappropriate correlations:
“In hiring, the bank wants to use AI to help source the right candidates. But executives have reservations.
“There’s a chance AI models will be biased,” said Caroline Arnold, BofA’s head of enterprise technology (which includes HR tech). “You might say, who’s going to be successful at this company? An AI engine could find that people who golf are going to be successful at the company. On the other hand, using those same techniques can remove bias if you have the model ignore some of these things that are indicators of different groups but go on to the meat of the profile of the person and understand it in a deeper way.”
Arnold believes an AI engine can never be the final say in who gets hired.
Mehul Patel, CEO of Hired, a technology company whose software uses AI to match people to jobs, agreed that AI and humans have biases.
“The good news about AI is, you can fix the bias,” he said. “We will boost underrepresented groups. The trouble with humans is they can’t unwire their bias easily. Human bias far outweighs algorithmic bias. That’s because we humans make quick decisions on people that aren’t founded on what you’re looking for in the job.”
Aditya Bhasin, head of consumer and wealth management technology at Bank of America, also shared reasons the bank is careful implementing AI.
“We’re early in the Gartner hype cycle on machine learning and robotics,” he said. “Everybody that was a big data company a few years ago is now a machine learning company.”
AI is not always the right solution, he observed.
“Technology is only useful for us when it’s in the service of meeting a client need to enable them to live a better financial life,” he said. “That’s what we constantly work with developers on.”
A robotic process automation solution that automates a loan process so the bank can deliver the loan faster to a client would be great, he said. But using AI or robotic process automation as a shortcut to data integration might not make sense, he suggested.
With the digital mortgage Bank of America recently launched, for instance, “we could have done a whole bunch of robotics to go and pull data from different places and prepopulate the mortgage application, [but] it probably would have been fraught with error,” Bhasin said.
“Instead, we did the hard work to integrate data feeds from multiple different sources so when we’re creating that digital mortgage application, there might be 170 fields and only 10 or 15 of them have to be filled out by the client, because all the other stuff we already know,” he said.”
The rest of the article highlights areas the bank is investing in AI tech, including chatbots, fraud detection, real-time analytics, dispute management, and providing financial insights to clients.
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
