JPMorgan doesn’t want to get burned by AI and machine learning. Here’s how it avoids costly mistakes.
The first thing Samik Chandarana needs you to understand is that machine learning will not answer all your prayers.
That may be unwelcome news to the thousands of executives who have seized on the buzzword and its cousin, Artificial Intelligence, to make their old companies sound new again. And it certainly sounds odd coming from someone like Chandarana, who has worked in JPMorgan’s corporate and investment bank since 2017 as head of data analytics, applied artificial intelligence, and machine learning.
But it’s a key lesson when one considers the increasingly lofty expectations for new tech. Wall Street saved $41.1 billion using AI in 2018, according to an April report from IHS Markit, and AI’s business value is seen reaching $300 billion globally by 2030.
So how exactly does a company deploy the technology?
Chandarana — along with his fellow JPMorgan executives Lidia Mangu and Manuela Veloso — has taken a measured approach. After careful consideration, Chandarana decided to position the tech as a support system for business lines within JPMorgan’s investment bank as opposed to dictating how, when, and where it should be implemented.
One example is DeepX, a market-making algorithm previously named LOXM that uses machine-learning techniques to decide when to execute orders and in what size, depending on market liquidity. The project was originally conceived by the equities trading unit and pulled in expertise from Chandarana’s operation as needed. The technology went live in 2017.
“They had the main expertise, they knew the business.” Chandarana told Business Insider. “My day-to-day contribution in terms of what they were already doing is quite low aside from making sure that the centralized resources and resource pool that I’m bringing together is there to help them accelerate.”
That’s not always the case. Sometimes senior executives will hear about something new and force subordinates to find ways of working with it, and the technology becomes more of a marketing tool than something of use to the business.
Just look at Blockchain, the decentralized ledger technology that’s been used for breeding and collecting digital cats and tracking lettuce.
A lot to lose
With a reported tech budget of $11.4 billion in 2019, JPMorgan has Wall Street’s biggest war chest for investing in AI and machine learning. That also means it has a lot of money to lose if it does not develop and apply the technology wisely.
“You can’t build a utility for the sake of a utility,” Chandarana said. “You build it up use case by use case and make sure it has some form of commercial impact at each point.”
Chandarana has developed a three-layer approach to evaluating, testing, and incorporating AI techniques. He oversees one layer, while Mangu, JPMorgan’s head of machine-learning center of excellence, and Veloso, the bank’s head of AI research, sit atop the others.
Chandarana runs the bottom layer, which pairs data scientists with