November 2, 2024

Karenmillen Outlet

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Why Finance Giants Like Goldman and Blackstone Want to Perfect Search

Why Finance Giants Like Goldman and Blackstone Want to Perfect Search
  • Finance firms and fintech startups are trying to use generative AI to improve search capabilities.
  • Search is a complex and challenging technical problem, but AI could make it a lot easier.
  • Here’s how firms like Goldman Sachs and Blackstone are trying to crack the code on search.

If data is the new oil, then banks like Goldman Sachs are in good business.

For decades, Goldman hoovered up information about the clients it did business with, trades executed, every dollar invested, and each loan financed. With that, coupled with external data from Bloomberg and Nasdaq, the hope was to supercharge the bank’s analytics engine and give its investment bankers, traders, and salespeople an edge.

But that fuel is useful only if you can access it.

For banks, much of that data got stowed away, usually to be found only if an employee knew exactly what they were looking for and where to find it.

That could all change as Goldman Sachs rolls out a generative-AI chat interface on its firmwide data platform, its chief data officer, Neema Raphael, told Business Insider.

Goldman employees can ask a question in plain English and let the artificial intelligence do the digging. In answering questions, the tool, called Legend AI Query, could pull information that even its users didn’t know existed.

The chat interface, combined with the bank’s data stockpiles, “gives you this sort of information superintelligence to help the human build a better mental model faster and quicker with more sources,” Raphael said.

It’s the latest development in Wall Street’s efforts to crack the code on search.

From Goldman Sachs to Blackstone, the biggest finance firms are using generative AI to make better use of their mountains of data. Even though it’s been decades since Google introduced the world to effective search to use in everyday life, only recently have financial firms started putting resources behind improving how employees tap their internal data. They’re trying to turn the wonky and sometimes impossible task of searching for information into a seamless process that will supercharge employee productivity. Perfecting search, down the line, could lead to more automation and more complex generative-AI tools.

Search is just the beginning

Goldman’s peers across the Street have their own search-related initiatives underway. JPMorgan’s private-bank AI copilot helps advisors track down information in real time. Bank of America’s Banker Assist aggregates internal and third-party info to give employees insights. Morgan Stanley’s AIMS helps advisors search the bank’s internal content.

While enabling employees to quickly get answers hidden in a vast amount of data is likely to supercharge worker productivity, there’s probably an even bigger ambition behind these efforts.

Pulling the right information and having some contextual understanding are the first steps in tapping into more complex use cases, Keri Smith told BI. At Accenture, Smith helps financial firms strategize and execute on their data and generative-AI initiatives.

“The power of enterprise search lies in its ability to save time so that humans can innovate and interact,” Jeff McMillan, Morgan Stanley’s head of firmwide AI, told BI. “Further, it lowers the barriers for employees to access robust intellectual capital quickly from the firm’s top experts, essentially arming them with knowledge firepower for meetings and discussions.”

A new class of fintechs is starting to emerge to sell Wall Street on cutting out simple but time-consuming tasks, like perfecting company logos on an investment-banking deck and prepping execs ahead of client meetings.

Rogo is one such startup. It offers an AI assistant capable of junior-banker-level duties and has already onboarded about 25 Wall Street firms onto its generative-AI platform.

“Firms are realizing the value of enterprise search is not just a typical search engine but for all the downstream applications you’re going to be able to build on top of it,” Rogo’s cofounder and CEO, Gabe Stengel, told BI.

Meanwhile, two Stanford grads came together to build Mako, a generative-AI associate for the private-equity industry. The startup, which aims to help employees search institutional data, recently raised $1.55 million from the same venture-capital firm that was an early backer of OpenAI.

Why search is hard

There’s a reason Google is the go-to search engine for the internet.

“It’s actually just fundamentally a difficult problem to suck out and then rank what might be useful” to a specific user, Raphael said of search. Additionally, it’s no easy feat to personalize relevance and deal with account permissions (who is authorized to see certain data), he added.

The latter is something Blackstone spent the majority of 10 months figuring out as it recently built its own internal AI-powered search engine.

And the fact that Wall Street lingo is complex and nuanced — words like hedge, ticker, and options have different meanings outside a financial context — presents another hurdle for financial firms using off-the-shelf products, such as OpenAI’s ChatGPT.

In March, Balyasny Asset Management hired Peter Anderson, a former Google AI scientist, to help the hedge fund level up its back-end system that pulls information from millions of documents to answer complex research questions. Familiarizing OpenAI’s models with financial jargon meant Balyasny’s internal version of ChatGPT surfaced the most helpful document 60% more frequently than without this training, the firm said.

Still, generative AI is bringing companies one step closer to solving search.

“People are building knowledge bases. They’re letting this gen AI crawl and to be able to either search or summarize. I think this is maybe a stepping stone to crack the problem,” Raphael said.

Where Goldman is throwing its generative-AI weight

Legend AI Query is just the beginning for Goldman Sachs.

The search tool is the bank’s second such generative-AI tool, the first being a generative-AI developer copilot that helps software engineers code more efficiently. The effort resulted in a roughly 20% increase in efficiency depending on the use case, a person familiar with Goldman’s generative-AI initiatives said.

At the same time that Goldman’s AI/machine-learning engineers were busy trying to crack the code on search, they built a generative-AI tool that aims to help data engineers — the developers who handle the bank’s data and make sure it is vetted, organized, and structured — do their jobs better. Legend Copilot, launched this month, is another tool, which is designed to make it easier to get more data onto Legend and manage it in a methodical way.

Raphael said he’s focused on “really helping both engineers and nonengineers find and discover the right data for their use case.”


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