Wall Street’s open secret: top analysts spend hours on data entry instead of generating insights.
People cannot believe me when I tell them how much of a Wall Street analyst’s job is manual data entry.
As a former buy-side analyst at Point72, I’ve experienced firsthand the frustration of manually updating financial models during earnings season.
Imagine it’s earnings day and you have to cover a slew of companies that are reporting. As companies start to release numbers you find yourself knee-deep in Excel, copying numbers from PDFs so you can prepare to ask insightful questions on a conference call.
I’ve been there, carefully inputting numbers from quarterly reports, 8Ks, and investor decks, feeling the weight of responsibility to get it right and the time pressure as I rushed to update financial models for multiple companies.
Hours were spent auditing models, filling data gaps, and checking for errors—slow, repetitive work that took time away from the insightful analysis. But it had to be done, and fast.
Some companies outsource these manual updates to third-party firms. This takes some of the burden off of the analyst, but it doesn’t solve the need for speed, and accuracy is always a concern. Manual work is still manual.
Five years ago, my Daloopa co-founders Jeremy Huang, Daniel Chen, and I decided to solve this problem.
Daloopa was created with an AI-first approach. We knew our solution needed to be the deepest, cleanest dataset available, but it also needed to be fast—not just a faster horse, but a whole new vehicle for financial analysis.
We leveraged AI as a catalyst because it was the best tool for solving the problem of speed for our customers, without compromising on completeness or accuracy. It was an unusual bet, AI was not cool then and the world was focused on RPA and rule-based approaches to software development. We saw the ability to leverage AI to create outsized improvements to speed and quality for financial data extraction.
AI automates the hard work of sourcing, organizing, and seamlessly delivering data to analysts.
All the data from regulatory filings goes in. Investor presentations. Footnotes. KPIs. Operating Data. You name it, our AI model is crawling it and pulling all of that data into a database.
You can access the data through our Daloopa Data sheet, which gives you all the historical spread on your coverage in a single Excel file, available on our marketplace.
Or you can use our Daloopa Add-In to update your model every time a company reports earnings–in your format, in your style, with no need for editing or formulas.
We’ve transformed model updates from hours of manual labor to a single button click, all while enhancing the quality and depth of analysis through our sophisticated AI models.
By automating the hard work of sourcing, organizing, and seamlessly delivering data, we’re freeing analysts to focus on what truly matters—insightful, value-adding analysis.
