Big banks are starting to be swayed by confidential computing providers and it could lead to major advances in fraud protection or marketing customization.
Confidential computing – which protects data while it’s actively being processed, not just when it’s stored or transferred – is making inroads with big banks for use in marketing, fraud detection, and cybersecurity.
Confidential computing is starting to be adopted at major banks, thanks to efforts from cloud providers like Microsoft, Google, and IBM.
This form of encryption allows data to stay secret even when cloud servers are analyzing it, which gives banks the ability to share information about transactions without making personally identifiable information and other data visible to partners or competitors.
Pooling encrypted transaction data could allow big banks to use machine learning to flag unusual patterns that could point to fraud or money laundering. In that way, confidential computing allows a bolder approach to collaboration among banks without running afoul of regulations.
Alternatively, it allows banks to share data with merchants, so that they can better target consumers for advertising without invading their privacy. For example, RBC uses confidential computing with Microsoft Azure to combine its own transaction data with merchant data, run machine learning algorithms to figure out how to best target consumers, and ultimately provide real-time, personalized ads without letting either side have access to new, unencrypted customer data.
JPMorgan security director Matt Novak recently described confidential computing during a conference talk as set to revolutionize cloud computing in finance: “Confidential computing is to traditional cloud what traditional cloud is to legacy on-premise datacenters,” he said.