AI has the potential to radically transform financial services. But first banks need to get their data in order.

Samantha Lee/Business Insider

Chatbots and fraud prevention applications are becoming more common, but banks are still doing the essential data work needed so AI can do the more ambitious work around investment recommendations.
JPMorgan, with its reportedly huge budget for technolgoy, drives innovation through the business lines rather than from IT, including its DeepX a market-making algorithm, that uses machine learning techniques to decide when to execute orders and in what size, depending on market liquidity.
This article includes an overview of AI in finance, including the top three trends to watch, and how JP Morgan is taking a measured approach to AI innovations.
Read how AI is transforming health, transporation, consumer technology, and more in other articles from our special report, How AI is Changing Everything.

Artificial intelligence has the potential to radically transform the financial services industry and firms are spending billions on the technology with hopes it will result in smarter decision making, better customer service, reduced fraud, and, perhaps most importantly, massive cost savings.

Research firm Autonomous estimates the industry broadly will be able to shave costs by as much as 22%, or $1 trillion, by 2030 through the use of AI.

It’s still early days, though, and right now, applications of the technology are far less sweeping. Big banks like Bank of America and Capital One are using AI for chatbots and voice assistants that can help customers easily answer simple questions about their finances (such as pulling up their account balance or scheduling a payment). PayPal is using similar tech to help customers get more details about transactions they’ve made, so they can avoid having to pick up the phone and speak to a human at the company — savings of about $25 million a year.

Firms are also using the tech to make better informed decisions about who to lend to, by capturing a more comprehensive, data-backed snapshot of a potential borrower, rather than just relying on a credit score. Credit card company Discover Financial Services, for example, is teaming up with AI software startup ZestFinance to improve lending decisions for personal loans and cut default rates by looking at unusual characteristics such as a history of discount store shopping.

Banks are feverishly cleaning and organizing data sets, forming data lakes and embracing the cloud technologies needed to measure massive data sets. Execs hope their machines will eventually be able to scour years of market data or Bloomberg chats to deliver clients tailored trades or investment ideas.

On the investing side, star portfolio managers from big quantitative hedge funds like DE Shaw and TwoSigma are using AI to crunch massive amounts of data to find signals and patterns too difficult for even large teams of analysts to pick out. The hope is that hedge funds can make better investments decisions then simply relying on intuition and fundamental research as they have historically done, and help reverse a trend of tepid returns. It remains to be seen if these bets are panning out.

The Eureka AI Hedge Fund Index which tracks performance of firms using AI posted returns of -5% in 2018, which was lower than the broader hedge fund index that saw -4% returns. The S&P 500, meanwhile, These use cases are small potatoes though for the sweeping impact that some believe AI will have, and it still remains very early for the industry.

Said Gordon Smith, the co-president and chief operating officer of JPMorgan at a recent financial conference, "Machine learning, artificial intelligence, I would say we at least are right at the beginning of that journey."

ShutterstockTop 3 opportunties for AI in financial services

Fraud prevention: Growing

Banks are increasingly using AI to stop financial crime. Current rules-based approaches for flagging suspicious accounts or transactions are notoriously flawed and result in lots of useless, time-consuming false positives. AI has the potential to suss out and adapt to criminal-behavior patterns more quickly and efficiently.

Improving and streamlining customer service interactions: Growing

Banks are using algorithms or chat bots to handle easy, quick questions to save time and money in their call centers. The goal of these voice assistants is to get smarter over time the more they speak with customers so they can deliver personalized insights. The goal, in time, is to provide sophisticated financial advice by analyzing your spending habits.

Banks are also working with startups that employ machine learning to automate for customers the process of redeeming rewards and benefits.

Enhancing investment processes: Nascent

Through the use of AI, hedge-fund portfolio managers can review as much as 1,000 times more data than they could before. While humans who will still be making the trades, data is increasingly being used to help funds make smarter investment decisions. If hedge funds can efficiently analyze data points from SEC filings and political speeches without having to actually read the documents, that’s time freed up for them to spend talking with CEOs and building models. For quant funds in particular, it increases the amount of data that goes into the trading algorithm and how quickly the algorithm can change or react to changes in the market.

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 might 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 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 unitCK, 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.

With a reported tech budget of $11.4 billion in 2019, JPMorgan has Wall Street’s biggest war chest to potentially invest in AI and ML. That also means it’s got 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 artificial intelligence 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 front- or back-office colleagues working directly with internal or external clients. From markets and payments all the way through to operations, data science teams work with specific business groups to understand what they do and how artificial intelligence might help them.

Chandarana wanted to avoid creating parallel teams for problems that would be better tackled together.

"You’re guiding people who have relationships and deep-seated knowledge about the business to try and advance the agenda and co-design the solution with the businesses they serve," Chandarana said.

Mangu, who spent 17 years at IBM’s Watson Research Center before joining JPMorgan, leads the middle layer, which consists of technique experts in various disciplines of artificial intelligence. Whether it’s natural language processing, speech-to-text, or deep learning, the team supports the first layer by translating white papers or research into useable computer code that can be deployed across the bank.

While Mangu’s team stays on top of the most current themes, Chandarana said the bank won’t use a technology simply just because it’s trendy.

"A lot of people use the words cutting-edge or bleeding-edge technology," Chandarana said. "We owe it to our customers to deploy technology in a controlled manner. Therefore it’s about appropriate technology not always classified as cutting or bleeding edge. The word ‘appropriate’ is important."

The top layer is the most theoretical. It is made up of the research team, led by Veloso, a top academic who was hired by the bank in 2018. Her team tackles issues like the ability to explain how algorithms actually came to a solution, known as interpretability, and discussions around ethics and fairness. Veloso’s team also pitches in on projects in the first two layers where deeper research is needed.

And there’s further collaboration as well, Chandarana added. There is Rob Casper, the JPMorgan’s chief data officer, whose team plays a critical role by cleaning, collecting and collating the necessary data. Then there is Apoorv Saxena, JPMorgan’s global head of the AIX technology team, whose group offers the ingredients for AI projects from the technology perspective. Saxena plays a role in setting the appropriate resources and project timelines.

The goal of the strategy is to successfully implement AI technology, but it can be just as important to identify where the tech shouldn’t be deployed. JPMorgan has set up a system of logging each project. Whether it’s a matter of the data not existing in the proper form — or at all — or a project that isn’t worth pursuing, everything needs to be written down so the same mistakes aren’t repeated. And employees need to be able to find it.

"Making that discoverable means that we’re just a little bit better educated than we were yesterday," Chandarana said. "Maybe that means people can collaborate and come up with new ideas about how to attack problems. Or maybe that means they know not to attack it at all because it’s already been done."

The Takeaway

Goldman Sachs

"AI and machine learning have gotten to a point where these bots can start to answer very complicated questions very quickly. To the extent this can help us scale distribution, that is a totally big revolutionary change to our business."

Adam Korn, co-head of Securities Division Engineering at Goldman Sachs

– Additional reporting by Bradley Saacks

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