• Flexi Group

Inside three data-science experiments at Goldman Sachs, UBS, and Citi.

Investment bankers are constantly on the hunt for new strategies to win over customers.

However, technology hasn't always been at the forefront of people's minds, especially among industry veterans who were hired when the position required old-school data mining methods.


According to Ronald Jansen, the head of UBS's worldwide financial-data center, a tidal change is upending banking. He's seen a trend away from old-school thinking and toward a much more progressive approach to accepting new technology.


"The most senior bankers recount stories of going down to the library and pulling up a stock-price chart for a firm," he told Insider.


But, he claims, it isn't enough these days.


"When we provide our clients advise, they want us to be armed with the most up-to-date knowledge," Jansen added. "The wind is blowing in the proper direction, and the tide is rising, assisting us in our progress."


Banks' increased dependence on data to achieve a competitive edge has sparked a hiring binge, according to Matt Stabile, the manager of the recruiting team for data science and engineering at the search agency Averity.


"Everyone is seeking for that edge that would propel them forward and ahead of their competition," said Stabile.


"The old-fashioned method of doing things, by instinct or maybe with fewer quantities of data," he said, "is just going to result in people not making the most informed judgments possible."


On Wall Street these days, data scientists and software engineers are coveted commodities. Consider JPMorgan Chase's recent job postings for a vice president focusing on data management in the corporate and investment bank's data and analytics branch.


The job stated, "Data is a critical objective and heightened focus of the CIB."


Within its CIB division, a spokeswoman for the bank told Insider that the bank was actively hiring for a variety of software and data engineering roles.


According to Geoffrey Horrell, global head of innovation and laboratories at the London Stock Exchange Group, banks are utilizing a number of data assets, ranging from mobile to satellite data, to perform a variety of duties. According to Horrell, the data is being used to advise on strategic mergers and acquisitions and to identify which clients to pitch.


A bank may, for example, utilize sales data to promote a consumer-goods firm that is seeing great growth in South America.


"Perhaps it implies they'll have more funds to acquire targets," he speculated. "Where are the targets increasing in number? Maybe it's in South America, because our research indicates that this is a promising market."


Recruiters claim that the talent war is raging.

Investment institutions are searching for "full-stack data scientists," according to Jay Bevacqua, an assistant vice president at the search agency Selby Jennings. These data whizzes are skilled in processing and analyzing large data sets as well as reporting crucial discoveries to investors.


Engineers with skills in software, machine learning, and artificial intelligence are in high demand, according to Jeanne Branthover, a managing partner at the recruiting agency DHR International. "They're in high demand right now, and every company is seeking for somebody like them."


In a recent Refinitiv study of senior bankers, 46% said they thought new tools and data will lead to better deal execution, while 66% said they hoped these resources would lead to enhanced data quality and accuracy in dealmaking models.


Insider spoke with three investment banks — Citi, Goldman Sachs, and UBS — to learn more about their data initiatives.


Citi's top data scientist in the banking, capital markets, and advising division, Daniel Costanza, takes a simple approach to data investment.


"Take the conventional questions that investment bankers are asked and look for spots where we can either provide better answers by putting data behind them or back current answers with a little more openness and impartiality," he added.


"There's a lot of pressure to keep innovating and to deliver the best service we can to our clients," said Costanza, who joined Citi in 2017 and was elevated to managing director last year.


One area where his data-science team has proven to be quite beneficial is in advising customers on environmental, social, and governance issues.


In recent years, ESG has pervaded everything from business valuations to corporate promises to reduce emissions. However, determining how well firms are at fulfilling their public pledges has proven difficult.


ESG reporting, according to Costanza, is "a bit of a muddle," with ambiguity in available data playing a large role. Citi has a chance as a result of this. The bank can assist customers determine which sustainability-ratings providers important in enticing investments and enable them in enhancing their ESG profiles by simplifying the procedure for sustainability-data reporting.


Costanza's team now has a presence on both sides of the Atlantic. In the United States, there are now dozens of data scientists, up from only Costanza in 2018.


Sukrita Chatterji, the data-science head, and her team in London serve clients in Europe, the Middle East, and Africa. Sustainability is a particularly important concern for businesses in these areas, according to Costanza.


Chatterji's four-person team "allows us to create connections with local bankers and clients while also allowing us to understand the flow of local advising work, allowing us to construct more targeted analytics," he added.


In addition, there is plenty of room for the London team to expand.


"If people see the value and demand more than we can give," he added, "we'll try to expand the staff to fulfill that need." "However, demand must always outpace supply."


Data, according to Costanza, is not meant to replace bankers' gut intuition.


"Human judgment from extremely brilliant, skilled individuals is the most essential element in our industry," he remarked. "We think it really makes the actual advice component of what we do that much more effective," he continued, when combined with the power of data science.


Goldman Sachs' core technical team is at the forefront of the investment bank's innovation.


The team lays the groundwork — infrastructure — for the development and maintenance of software. Cloud computing, data structure and modeling, information security, and machine learning are all areas where Core Engineering works. The group collaborates with all of the company's business segments.


According to Miruna Stratan, a Goldman Sachs partner who heads up engineering for the investment banking business, it's an area where the firm is focused on hiring.


The procedure enables the bank to innovate more quickly. Once the infrastructure and software tools have been delivered to the business units, their own teams can modify the technology with unique features and analytics to match their individual demands.


The firm may free up other developers' time because the separate business divisions don't have to design and manage their own infrastructure. Internal efficiencies are created at a bank where engineers account for 25% of the workforce.


"You had to create everything yourself a few of years ago," Stratan told Insider.


The core engineering team's work with the public cloud, for example, has "enabled us to swiftly deliver business functionality without the need to develop the whole infrastructure stack," according to her.


Goldman Sachs' senior executives have been focusing on the move to the public cloud. The investment bank's cloud-based apps and tools all began with the support of core engineers during the last three years.


By relying on its core technical team, Goldman Sachs' investment bank has been able to deliver new technologies that aid in deal prospecting, opportunity targeting, and pitching process innovation.


For example, the bank developed algorithms to assist bankers in spotting and prioritizing transaction prospects for customers by watching certain variables within customer accounts in order to anticipate their demands. For example, if the algo detects that a client's balance sheet has reached a specific level, it might indicate that the customer is ready to refinance or issue debt.


And, as the bank strives to automate certain time-consuming portions of the process, such as pulling in data based on pricing, stocks, and interest rates for analysis charts, selling customers on new opportunities has become more efficient.

The head of UBS's worldwide banking-data lab, Ronald Jansen, has a thesis: human intuition paired with the accuracy of data science may produce strong outcomes.


"Software, data science, and technology are fantastic at assisting with the analytical side of things," he remarked.


But, he warned, they're still not a replacement for human judgment when used in isolation.


"In order to be a very good counsel, you need to combine these two elements together," Jansen added.


UBS-Guard, a software application developed by Jansen's team last summer to assist public businesses in fending against activist investors, is one of the group's creations.


"Global Utility for Activism Risk and Defense" is the program's acronym. The program examines a company's basic flaws that activist shareholders may exploit, alerting management teams so that defense actions may be implemented ahead of time.


"In one case, our activism team used UBS-Guard to identify the drivers of possible vulnerabilities for a customer, and then test a range of 'what if' scenarios that might lower the risk of being targeted by an activist," Jansen explained.


To evaluate the possibility of shareholder activism, Jansen said the Guard software took into account more than 320 million unique data points and over 5,000 previous activism efforts.


Some of Jansen's data professionals are former investment bankers, while others are fresh out of college data scientists and software engineers. The purpose of the global data lab, according to Jansen, is to use data science and predictive analytics to "systematically find deal possibilities, engage with customers in new ways, and help our bankers work more successfully."


When bankers approach Jansen's team with customer demands, ideas for product development are frequently developed.


"From there, we establish cross-functional teams that draw on the talents of our data scientists as well as the knowledge of our bankers," he explained, adding that the success of UBS-Guard has encouraged additional bankers to join his team.


He stated, "The world around us is continuously changing." "We have to remain on top of things."


By Flexi Team


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