Article
Artificial intelligence in banking — separating reality from hype
AI and machine learning for banking are getting a huge amount of attention these days, but the question remains: can these technologies truly replace human interaction and in-depth analysis of data?
August 6, 2019 by Amy Castor — Editor, Networld Media Group
"I'm sorry Dave, I'm afraid I can't do that."
Who can forget those iconic words spoken by HAL 9000, the onboard computer that took over the spaceship in the 1968 film "2001: A Space Odyssey."
It's an over-the-top example of the hype surrounding artificial intelligence and machine learning — two technologies that generally refer to the ability of computers to assume tasks that normally require human decision making.
We're certainly not at the point were AI is threatening to take over our spaceships, but in the banking world, the technology is being touted as the solution for everything from loan underwriting to digital security and customer service. And sometimes it's hard to know what solutions are realistic versus "way out there."
One banker thinks the real promise of AI is in eliminating tedious, repetitive jobs, freeing people up to focus on decision making and human interactions.
"Most AI use cases center around automating mundane tasks with robotic process automation, fraud mitigation, natural language processing and complementing business intelligence," Ram Ridgeway, VP of digital experience at Advia Credit Union, told ATM Marketplace.
Ridgeway, who will speak on the panel "Real Value or Real Hype: AI, Machine Learning and the Future of Customer Data" at the annual Bank Customer Experience Summit in Chicago, Sept. 23-25, disagrees that the technology is over hyped.
"I'd say each of the use cases have varying levels of complexity," he said, noting that some use cases, like business intelligence (the processes of collecting and analyzing data) have a long way to go before they demonstrate real value.
For example, most bankers understand the value proposition of being able to use AI to mine data for contextual insights. Applying the technology is another story, however.
"In reality, we all have messy, unstructured data and the work to remedy the problem is daunting," Ridgeway said. "If and when we do reach the 'BI promise land,' will the result be worth the investment? That is an outstanding question."
And it's not just AI's role in business intelligence, he said. The same can be said about all aspects of AI and machine learning. Financial institutions have to weigh what they invest into a technology, versus what they hope to get out of it.
In the meantime, a lot of real work is happening in machine learning right now.
In the banking world, "we have people mining data for insights, which can be automated with AI. We have team members looking for fraud trends, which can be automated with AI. We have team members answering the same repetitive questions, which we are now being automated through AI. And that's allowing us to reallocate human resources to deepening member relationships," he said.
Ridgeway believes AI is here to stay and that it will continue to evolve in the financial industry. As for its success? That will "largely depend on the level of buy-in and alignment among the varying business lines of an organization," he noted.
About Amy Castor
Amy Castor has more than 20 years of experience in journalism and mass communications. In the last several years, she has gotten particularly interested cryptocurrencies, blockchain technologies and other evolving forms of payment. Her work has appeared in consumer and trade publications throughout the U.S., including CoinDesk, Forbes, and Bitcoin Magazine. She is now the editor of ATMmarketplace.com and WorldofMoney.com
