Daloopa trains artificial intelligence to automate the work processes of financial analysts

Thomas Li worked at Point72, the hedge fund he founded notorious investor Steve Cohenwhen he realized that the financial industry relied heavily on manual data entry processes that could be prone to error.

«As a buy-side analyst, I felt the pain of manually finding and entering data to build and update financial models,» Li told TechCrunch. «It took my time away from the more important work of analysis and investment.»

After meeting Jeremy Huang, a former Airbnb and Met software engineer, and Daniel Chen, a former Microsoft engineer, through connections at New York University (all three are alumni), Li decided to try his hand at an automated solution to data entry challenges.

Three partners started Daloop, which uses AI to extract and organize data from financial reports and investor presentations for analysts. Daloopa announced Tuesday that it has raised $18 million in a Series B funding round led by Touring Capital, with participation from Morgan Stanley and Nexus Venture Partners.

«Daloopa is an AI-powered historical data infrastructure for analysts,» Li said. «This way of approaching the discovery process keeps highly competitive companies and teams ahead of the curve.»

Daloopa’s clients are primarily hedge funds, private equity firms, mutual funds, and corporate and investment banks, Li says. They use the startup’s tools to build investment and due diligence workflows. The workflow, powered by artificial intelligence algorithms, discovers and delivers data to analysts’ financial models, reducing the need for manual data copying.

«Daloopa provides a new way to obtain mission-critical data on both the buy-side and the sell-side,» said Li. «The time saved is reinvested in research and analysis or client time – helping our clients gain an edge in their research process.»

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I’m a bit skeptical that Daloop’s AI isn’t wrong: after all, no AI system is perfect. Thanks to a phenomenon known as hallucinationit is not uncommon for AI models make up facts and figures when summarizing documents and files.

Li did not suggest that Daloopa was safe. But he argued that the platform’s algorithms «just continue to improve over time» as they are trained on ever-larger sets of financial documents. Mom is talking about where exactly the data is coming from; Li only says it’s from «public sources such as SEC filings and investor presentations.»

«Daloopa has been an AI company since its birth five years ago, before all the AI ​​hype,» Li said. «We spent those years training our algorithms and developing AI for financial institutions.»

With the new funding, which brings New York-based Daloopa’s total to $40 million, the company plans to grow its team of ~300 employees, strengthen product R&D, and expand its customer acquisition efforts.

“Daloopa is an AI-powered solution that started ahead of the curve and has seen accelerated year-over-year growth over the past two years,” he said. «As financial institutions increasingly embrace AI tools, we are very well positioned to be a leader in the AI-powered master data space.»

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