Security News > 2020 > November > Banks looking to confidential computing for solutions to money laundering, theft, and fraud
"The traditional processes that banks and other financial institutions employ are a bit resource-intensive and often those processes are manual so they've innovated to add some software modeling in an attempt to draw inferences from patterns that they see in their transactions and in their operations to help inform a potential theft, fraud or money laundering," said Michael Reed, director of the Blockchain Program at Intel.
"But even those have high false-positive rates which can be disruptive to their customer relationships and also just cost them more time and money in their operations. Confidential computing is a new tool that banks and other corporations are using to help address their own security challenges. It's an emerging technology that helps secure data while it's in use."
"What confidential computing can do for any money laundering and banks is it can really turn it into a team sport. It allows companies to collaborate to get better results. The way they do that is they use a technique called federated learning or sometimes called federated machine learning," Reed explained.
IBM's work on confidential computing projects, he said, have underpinned decisions by banks like Bank of America, MUFG and BNPP's to select the company's cloud services.
"Confidential computing allows banks to maintain full authority of their data and workloads to meet regulatory compliance while ensuring the security, privacy, and integrity of their clients' banking information. As banks are being pressed to innovate faster to maintain a competitive edge, many are looking at cloud as a means to enable them to be more agile."