For all the fast adoption of artificial intelligence tools in so many facets of business, AI’s entrance into fintech applications has been slow so far. That’s largely due to regulatory and compliance hurdles related to handling people’s money, industry experts say. To date, most AI features in fintech have focused on speeding up functions like customer service, accounting and other back-office operations, with companies ranging from Klarna and Chime to Stripe and Ramp announcing new AI products. Now a new trend in fintech is emerging: using AI for deep investment research.
Many companies wading into this space are using AI agents—code that can understand contextual information, make logical decisions and take actions. Agents can perform tasks like making investment recommendations or creating draft PowerPoint presentations. Just over the past month, trading app Robinhood and Arta Finance, a startup that aims to be a digital “family office” by giving wealthy consumers access to alternative investments, have announced new consumer-facing AI features. An even larger set of emerging companies, nearly all of which seem to be based in New York, are using AI to speed up research for investment bankers and investors. They include outfits like AlphaSense, Hebbia, RavenPack and Rogo.
It’s hard to tell which of these businesses will live up to their promises and hype. Most are early-stage startups, and the venture capital frenzy for AI companies is still in full swing, making it even more difficult to predict which will build durable businesses. But they’re all addressing labor-intensive tasks where improvements are long overdue.
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Hebbia is a five-year-old New York company that uses AI to try to help financial institutions, law firms and other large companies speed up their research. It raised $130 million in funding last year at a $700 million valuation and is backed by investors including Index Ventures, Peter Thiel and Andreessen Horowitz.
Within financial services, Hebbia focuses on analyzing private market data, says 27-year-old founder and CEO George Sivulka. When companies are trying to raise new financing or considering being acquired, they’ll typically share confidential information in a secure, virtual data room made accessible to prospective investors. They’ll upload information like their audited financials, stock ownership, patents and disclosures on ongoing litigation. Hebbia’s software connects to data rooms and tries to answer questions about a company’s customer concentration (how reliant it is on a small set of customers), the strength of its revenue growth and the qualifications of its management team.
It also aims to identify risks and red flags like how exposed the business might be to tariffs, or whether a topic was glossed over or omitted in an investment pitch even though it’s typically covered. Hebbia’s software can then creates a draft memo based on its analysis. The company claims private equity firms can save 20 to 30 hours per deal using its products.
Like most startups in this space, Hebbia uses a collection of different AI models. It has its own models for retrieving and interpreting investment information from data rooms. It uses outside models from OpenAI, Anthropic and other companies (depending on the customer’s preference) for features like generating the text of reports. Sivulka says Hebbia has hundreds of customers, including private equity firm Charlesbank and private credit firm Oak Hill Advisors, and that it charges from “tens of thousands” to “millions” for its services.
AlphaSense, a 14-year-old financial data company with 6,000 customers, also uses AI agents. Like Hebbia, it aims to help analysts with preparing pitches, conducting due diligence research and analyzing markets. For example, if an analyst is preparing for a meeting with a large company’s CEO or CFO, AlphaSense can spit out research suggesting what the executive’s top priorities are, a spokesperson says. It can create a report using experts’ testimony to indicate the types of questions institutional investors are asking about Klarna ahead of its IPO roadshow.
Rogo is a 40-person, three-year-old New York startup with 40 customers and more than 5,000 active end users, says 26-year-old CEO Gabriel Stengel. According to PitchBook, it was valued at $350 million in a March fundraise, and it’s backed by investors including Thrive and Khosla Ventures.
Rogo can automate the summaries investment banking analysts need to write when a company announces quarterly earnings. Or let’s say a banker wants to pitch an idea for a large company like ServiceNow to acquire an AI startup in order to improve its own AI capabilities. Rogo can help create a presentation to summarize different AI companies that could be acquisition targets. “How do you want to compare all these different providers, the startups and the hyperscalers that have done this?” Stengel explains. Another example: Rogo can analyze dating app Bumble’s revenue by country by looking at the businesses it owns in different geographies and interpreting the CEO’s historical comments.
The startup uses its own models to retrieve and interpret data–it tries to train its models to think like an investor. It uses outside models from Google, Meta, Anthropic and OpenAI for other features. For example, if a given task calls for a statistical analysis like a regression, it can use OpenAI’s mathematical models.
RavenPack is a 22-year-old New York company whose primary business is using data analysis of news and regulatory filings to identify market-moving events for financial institutions like banks and quantitative trading firms. Its customers include JPMorgan, Morgan Stanley, Deutsche Bank and investment bank Nomura, says CEO Armando Gonzalez.
Today, it announced a set of new AI features aimed at a wider audience. Through Bigdata.com, analysts can use its AI to create stock watchlists that will send daily, automated research reports. It uses its own models to interpret and retrieve data for initial search queries, and it uses Anthropic to let users query specific documents, like regulatory filings. The site offers a free service with a limited set of data sources and search queries. A premium subscription comes with access to more data sources and starts at $50 a month.
The number of new companies popping up in this space is hard to keep track of. BlueFlame AI’s website says it helps alternative asset managers like hedge funds make better use of generative AI models. And there’s a burgeoning crop of tiny startups–most with fewer than 20 people–that’s quickly forming, including businesses like AgentSmyth, BrightWave, Finster AI, ModelML and ProSights.
AI features are coming to consumer apps, too. Late last month, Robinhood announced Cortex, a new AI tool for investing that it will launch later this year to customers who pay for its premium Gold service. Like Bigdata.com, it will produce automated reports on what’s going on with a stock. For the day-trader variety, it can suggest ideas for trades. The product “makes the options trading experience more intuitive by helping you translate your beliefs about a stock into a specific options trade and strategy,” according to a company blog post.
Last week, Arta Finance announced Arta AI, which will launch in “mid-2025” and makes personalized investment suggestions. It also answers questions like how your portfolio did last month and how a stock is performing. A subscription will start at $20 a month, and customers with more than $100,000 managed by Arta can get the service for free.