At the world’s biggest debt dealer, traders can have trouble making sense of all the action as it happens. So JP Morgan Chase & Co is bringing in artificial intelligence to give them a picture of the whole trading floor – and even predict where markets are going.
MSX, a data analytics and machine-learning program, is being deployed in the bank’s fixed-income sales and trading operations, New York-based JPMorgan said Monday in a statement. It will compile data from all desks and orders to give salespeople and traders a clearer picture in real time and help them anticipate market moves. Developed by London-based startup Mosaic Smart Data, the program is already used in JPMorgan’s rates trading.
JPMorgan, which generated $15 billion in revenue from its fixed-income business last year, is among banks introducing machine learning and artificial intelligence in capital-markets units. The latest move is about improving the performance of salespeople, not replacing them, said Mosaic Chief Executive Officer Matthew Hodgson.
“Automation of tasks doesn’t equal automation of jobs,” Hodgson said in a phone interview. It will be another tool a fixed income salesperson can use to make his or her job more efficient and provide better service, he said. Hodgson is a former managing director at Deutsche Bank AG and has worked at Salomon Brothers, according to his LinkedIn profile.
Mosaic previously partnered with JPMorgan through the bank’s “In-Residence” fintech startup development program created in 2016. Mosaic is the first company to complete the program, which gives startups six months of access to JPMorgan’s people, facilities and systems. “The Mosaic platform integrates securely with our existing technology infrastructure, and enables our teams to quickly make better informed decisions,” JPMorgan’s head of global macro trading, Troy Rohrbaugh, said in a statement.
The software can identify patterns in data that JPMorgan already generates. It aims to predict client behavior and offer them ideas they’re more likely to be interested in. “It’s the ability to measure the business at the empirical, rather than the anecdotal level,” Hodgson said. “If I’m really interested to know that, for instance, asset managers are active in a particular asset, I’d be very interested to know what maturity they’re contracting in.”