JPMorgan Employs Machine Learning For Expense Reports

JPMorgan Chase has been using artificial intelligence (AI) and machine learning to process expense reports and check them for compliance, according to Bloomberg. Lori Beer, JPMorgan’s global chief information officer, said that the move has helped with efficiency.

“We basically have eliminated manager approvals,” Beer said. “We’re doing 100 percent of audit[s] through a machine learning model that makes sure that, as we process travel and expense reports, they’re in alignment with our policies.”

Finance is a particularly difficult industry to police expense-wise because many workers spend time on the road, working with clients and taking them out. Last year, Wells Fargo got rid of a number of employees for lying on expense reports.

In addition, forcing managers to spend more time looking over reports, or hiring outsiders to do so, adds to expenses instead of curtailing them. By using AI, the company is “taking some bureaucracy out of our managers’ hands.”

The bank is using technology to cut costs and become more efficient in its processes, and in an attempt to tamp down on fraud, as well as make things better for its workers and customers.

Last year, the bank hired Manuela Veloso, Carnegie Mellon’s head of machine learning, to push the bank forward in this department. Earlier this year, CEO Jamie Dimon said that by using machine learning, the bank could save $150 million in terms of credit card fraud.

In other JPMorgan news, Japan is looking to JPMorgan Chase’s blockchain-based information network for payments. A country long blamed for weak measures against money laundering, Japan has over 80 banks interested in joining the Interbank Information Network (IIN), said Executive Director Daizaburo Sanai.

Japanese banks could be looking to use the platform to enhance anti-money laundering measures, as cash recipients can be screened “faster and more [efficiently],” Sanai said.