- Goldman Sachs is replumbing some of the internal systems used by bankers.
- Akila Raman is COO of the investment banking division. Miruna Stratan leads IBD’s engineering.
- Here are three ways the firm is automating workflows, from peer sheets to ECM data.
A perfect storm of surging deal flow and burnt out workers is pushing some investment banks to automate more.
M&A deals are getting done at record levels. And junior bankers, often the ones responsible for the grunt work required to close deals, are waning as firms strive to keep pace. Working remotely has exacerbated the situation, in addition to a labor shortage.
Now, firms are looking to technology to catch up, and maybe even get ahead.
While Goldman Sachs’ automation endeavors in its investment bank are not new, the past six months have fortified those efforts.
“It certainly made us re-evaluate and recommit ourselves in the sense that we were already on this path,” Akila Raman, chief operating officer of Goldman’s investment bank division, told Insider referring to the bank’s automation strategy.
Raman and Miruna Stratan, who leads the IBD engineering team, detailed three use cases the bank is eyeing for additional automation.
Peer sheets
Over the last several years, Goldman Sachs has worked to automate data scrubbing for company comparison tables, Raman said. The so-called peer sheets are used by investors to size up company prospects against competitors, often including market analyses and charts about pricing, stocks, and interest rates.
It’s a time-intensive process and a rote task that’s handled by junior bankers. But because many charts are built on market inputs, they’re obvious candidates to create a framework where inputs are automatically fed into the pages.
Peer sheets are one component of pitch books, whose assembly is often left to junior bankers and is an area ripe for automation.
While pitch books still hold value, and the process of assembling them won’t be fully automated, there is room for streamlining, Raman said.
“Especially if it’s a longer term deal process, the client may need to see a particular chart several times. So does somebody physically update that chart, and then copy and paste that into a pitch book, or can technology be levered to take some of those steps out?” Raman said.
Opportunity targeting
Goldman started using sophisticated algorithms two years ago to help bankers identify deal opportunities for clients, Raman said. The algorithms flag if a certain metric in a customer account has hit, meaning bankers can anticipate the client’s upcoming needs.
If a customer’s balance sheet hits a certain number, the algorithm may suggest the customer will need to issue debt, refinance, or could be a good candidate for equity issuance. The algorithm is designed to help bankers not miss opportunities and prioritize services.
It will also continue to evolve, as bankers can inform the model if a suggestion was helpful or not, giving it an opportunity to improve.
Consolidating legacy systems
Another ongoing initiative designed to lift banker work is a long-term project of consolidating data in Goldman Sachs’ debt capital markets.
To start, the bank is organizing data by financial instrument — like bonds, loans, and structured finance products — by unifying several legacy systems that are currently in place, Raman said.
Eventually, the bank will develop a single tool for the entire debt capital markets. This should ultimately save bankers time when collating and analyzing data, she added. Raman declined to disclose a specific timeline.
All of the work is done with a goal of receiving constant feedback in order to understand how to better make adjustments, Stratan said.
“You’ve got to be constantly challenging yourself to figure out what is truly value-add, and what is stuff that technology can do with equal or better impact,” Raman said.
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