Banking technology adoption: Taking a broader view

With financial institutions around the world increasingly adopting new technologies like artificial intelligence and Fintech, “it’s critical for industry leaders and policymakers to understand the impact,” says Alessandro Rebucci, professor of economics at Johns Hopkins Carey Business School. He notes that research to date has mostly focused at the “micro” level, on banks, technology firms, and household-level outcomes.

“But when we evaluate the cost and benefit of financial innovation, we need to look past the financial institution itself to the larger economy,” he says. To do just that, Rebucci and colleagues Sheila Jiang, a researcher at Amazon, and Gang Zhang, of Cheung Kong Graduate School of Business, have developed a novel model that examines what happens when banks adopt technology produced by entrepreneurial firms.

Using the model, the team found that higher levels of technology adoption on the part of banks can significantly lower lending rates and raise per capita GDP growth, from 2% to 2.17%. In addition, notes Rebucci, “We show that higher bank IT acquisition is associated with higher lending volumes to small businesses.” As financing becomes cheaper and its cost for these businesses declines, he says, “they can scale up.

‘A virtuous cycle’

At the core of the team’s model, which they shared recently in a working paper, is the interplay between banking efficiency and the productivity of the borrowing firms.

“Usually, the questions of how the economy grows and how financial systems achieve higher efficiency are looked at as two separate issues,” Rebucci says. “This is one of the first papers to look at these two factors together.”

What the team found in examining that interplay, he says, is “a virtuous cycle.” As productivity among technology firms increases, the price of the capital goods they produce (including those that embed new ideas and technologies) goes down, inducing banks to purchase more of those goods, thereby increasing the banks’ efficiency, which leads to further growth of firm production and bank efficiency.

The team tested their model using U.S. bank, metropolitan, and state-level data, including Call Report-Harte Hanks Market Intelligence data on banks’ expenditures on information technology. Consistent with their model predictions, Rebucci, Jiang, and Zhang found that banks with larger IT budgets have a lower cost of financial intermediation, or CFI, which is primarily driven by lower labor compensation rather than bank profitability.

“Our model also predicts that banks should lend more to entrepreneurs as they grow and become more efficient,” Rebucci says, “since this drives loan volume growth, which is needed to finance firms’ working capital.”

Reflecting the reality on the ground

Rebucci says a key inspiration for the team’s project grew out of recent research examining patent data. It showed that financial innovation is primarily produced by technology firms and not by financial institutions themselves. “The R&D is done by tech firms in Silicon Valley and then sold to banks; it’s not the banks setting up their own R&D departments,” he says.

This insight, central to the team’s model, reflects what Rebucci describes as “the reality on the ground,” lending relevance to their findings.

What are the lessons of this work for economists and regulators? “When we evaluate the costs and benefits of financial innovation, we need to look past just financial institutions themselves to consider the larger economic landscape,” Rebucci says.