Investment bankers are always on the lookout for ways to get a leg up on winning over clients.
But tech hasn't always been top of mind — particularly among industry veterans who came up when the job involved old-school methods of digging up data.
A sea change is upending banking, according to Ronald Jansen, the head of UBS's global banking-data lab. He's seeing a shift from an old-school mentality to one that's much more progressive in adopting new tech.
"The most senior bankers tell stories where they could go down into the library and collect all the Wall Street Journal pages and pull together a stock-price chart for a company," he told Insider.
But that won't cut it these days, he said.
"Our clients expect that we are armed with the best information when we give them advice," Jansen added. "The winds are blowing in the right direction, the tide is rising and helping us advance."
Matt Stabile, the manager of the recruitment team for data science and engineering at the search firm Averity, told Insider that banks' increasing reliance on data to gain a competitive advantage has spurred a hiring spree.
"Everyone's looking for that edge that's going to put them forward and put them ahead of their competitors," Stabile said.
Banks have realized, he added, that "the old-fashioned way of doing it, via instinct or maybe with smaller amounts of data, is just going to result in them not making the best informed decisions possible."
These days, data scientists and software engineers are among the hottest commodities on Wall Street. Just look at job posts from JPMorgan Chase, which was recently hiring for a vice president focused on data management within the corporate and investment bank's data and analytics office.
"Data is a key priority and heightened focus of the CIB," the posting said.
A spokesperson for the firm told Insider the bank was actively hiring for a variety of jobs in software and data engineering within its CIB division.
Banks are leveraging all sorts of data assets, from mobile to satellite data, to perform a variety of functions, according to Geoffrey Horrell, the global head of innovation and labs at the London Stock Exchange Group. They're using this data to advise on strategic mergers and acquisitions or determine which clients to pitch, Horrell said.
For example, a bank could use sales data to pitch a consumer-goods company showing strong growth in South America.
"Maybe that means they're going to have more cash to buy targets," he said. "Where are the targets growing? Well, maybe it's in South America because our data tells us that South America is a good market."
Recruiters say competition for talent is white hot
Jay Bevacqua, an associate vice president at the search firm Selby Jennings, said investment banks were looking for "full-stack data scientists." These data whizzes are adept at processing and analyzing complex data sets and reporting the key findings to shareholders.
"It's a candidate's market," Jeanne Branthover, a managing partner at the recruitment firm DHR International, said of engineers with software, machine-learning, and artificial-intelligence backgrounds. "They're in hot demand right now, and every firm's looking for these kinds of people."
In a recent Refinitiv survey of senior bankers, 46% of respondents said they hoped the adoption of new tools and data could lead to better deal execution, and 66% said they hoped that these resources would lead to improved data quality and accuracy in dealmaking models.
Insider caught up with three investment banks — Citi, Goldman Sachs, and UBS — to take a peek at their data projects.
Citi's data team goes global
Daniel Costanza, Citi's chief data scientist in its banking, capital-markets, and advisory division, has a straightforward philosophy behind investing in data.
"Take the traditional questions we get asked as investment bankers and find places where we can give either better answers by putting data behind them or support existing answers with a little bit more transparency and objectivity," he said.
"There's lots and lots of pressure to continue to innovate and to be able to provide the best service we can to our clients," said Costanza, a Goldman Sachs alum who joined Citi in 2017 and was promoted to managing director last year.
One area where his data-science team has found a great deal of usefulness is in advising clients on environmental, social, and governance considerations.
ESG has permeated everything from company valuations to corporate pledges to cut down on emissions in recent years. But measuring how successful companies are in achieving their public commitments has proved challenging.
Costanza said ESG reporting was "a bit of a mess" and a big part of that involved the ambiguity in available data. That's created an opportunity for Citi. By improving the process for sustainability-data reporting, the bank can help clients recognize which sustainability-ratings providers matter in luring investments and aid them in improving their ESG profiles.
These days, Costanza's team has footprints on both sides of the Atlantic. In the US, it's gone from just Costanza in 2018 to dozens of data scientists today.
In London, the data-science lead Sukrita Chatterji and her team serve clients in Europe, the Middle East, and Africa. In these regions, sustainability is a particularly pressing topic for corporations, Costanza said.
Chatterji's team of four "allows us to build relationships with local bankers and clients and see the flow of local advisory work, enabling us to build more tailored analytics," he said.
Plus, there's plenty of room for the London team to grow its head count.
"If people see the value and begin demanding more than we can deliver, we'll look to grow the team to meet that demand," he said. "But the demand needs to always lead the supply."
Costanza said data was not intended to replace the gut instincts of bankers.
"The most important thing of our business is human judgment from really talented, capable people," he said. Combined with the power of data science, "we think that just makes the actual advisory component of what we do that much more powerful," he added.
Goldman Sachs' special engineering group
For Goldman Sachs, innovation in the investment bank starts with its core engineering team.
The team builds the foundation — infrastructure — on which software is created and maintained. Core engineering's work spans cloud computing, data structure and modeling, information security, and machine learning. The group collaborates with business units across the firm.
It's an area where the bank is focusing on hiring, according to Miruna Stratan, a Goldman Sachs partner who heads up engineering for the investment-banking division.
The process allows the bank to innovate faster. Once core engineering delivers the infrastructure and software tools to the business units, their own teams can customize the tech with unique features and analytics to meet specific needs.
And because the individual business units don't have to build and maintain their own infrastructure, the firm can free up other developers' time. For a bank where 25% of employees are engineers, that creates internal efficiencies.
"A couple of years ago, you had to build everything yourself," Stratan told Insider.
For example, the core engineering team's work with the public cloud has "allowed us to quickly release business functionality without the need to build the full infrastructure stack," she added.
The shift to the public cloud has been a key focus of top executives at Goldman Sachs. In the past three years, the investment bank's cloud-based applications and tools all started with the help of core engineering.
Leaning on the core engineering team has allowed Goldman Sachs' investment bank to introduce new tools that help with deal prospecting, opportunity targeting, and innovating on the pitching process.
For instance, the bank created algorithms that help bankers spot and prioritize deal opportunities for clients by tracking certain metrics within customer accounts to anticipate their needs. If the algo flags that a client's balance sheet hits a certain threshold, for example, it may suggest the customer is ready to refinance or issue debt.
And pitching clients on new prospects has become more streamlined as the bank works to automate certain time-intensive parts of the process, like pulling in data based on pricing, stocks, and interest rates for analysis charts.
UBS helps clients get ahead of activist shareholders
Ronald Jansen, the head of UBS's global banking-data lab, has a thesis: Human intuition, combined with the precision of data science, can lead to powerful results.
"Software and data science and technology — they're really good at supporting the analytical side of things," he said.
But taken in isolation, they're still not a substitute for human judgment, he added.
"You need to put these two things together in order to be really effective as an advisor," Jansen said.
One brainchild from the group is UBS-Guard, a software program launched by Jansen's team last summer that's directed at helping public companies fend off activist investors.
The program's name is an acronym for "Global Utility for Activism Risk and Defense." The tool analyzes companies' fundamental weaknesses that activist shareholders might target, giving management teams a warning so that they can implement defensive strategies proactively.
"In one example, our activism team leveraged UBS-Guard to identify the drivers of potential vulnerabilities for a client," Jansen said, describing the tool's utility, "and then test a range of 'what if' scenarios that would reduce the likelihood of being targeted by an activist."
Jansen said the Guard program factored in more than 320 million individual data points and recorded over 5,000 past activism campaigns to calculate the likelihood of shareholder activism.
Some data experts on Jansen's team are former investment bankers, while others are data scientists and software engineers right out of college. The global data lab's goal, Jansen said, is to harness data science and predictive analysis to "systematically identify deal opportunities, connect with clients in innovative ways, and help our bankers work more productively."
Ideas for the development of products are often hatched when bankers approach Jansen's team with client needs.
"From there we form cross-functional teams that tap both into the skills of our data scientists and the expertise of our bankers," he said, adding that the success of UBS-Guard had incentivized more bankers to engage with his team.
"The world around us is constantly changing," he said. "We need to stay ahead of the curve."
