When AIs Speak Finance

Today’s featured startup helps analysts skip the grunt work and focus on insight, not input.

Project Overview

Daloopa has developed an AI tool capable of building financial models based on the financial performance metrics of companies.

In simple terms, you provide the platform with a business’s financial data, and it constructs a financial model that explains how the business operates. This model can be used to assess the sustainability of the company’s business model and to create forecasts for its potential development.

Currently, the AI tool works with the data of 3,000 publicly traded companies. It pulls reports from the U.S. Securities and Exchange Commission (SEC), earnings call transcripts, and other publicly available data sources.

The target audience for the startup includes investment funds, banks, private equity funds, and analytical companies.

What’s particularly interesting is the platform’s interface – an Excel plugin that generates financial models directly in an analyst’s Excel file. After the model is created, the analyst can continue working with it in the same way as usual.

A key feature is that every data point in the model can be traced back to its original source, which the AI uses. Daloopa’s motto is “Trust but Verify.”

After a new quarterly report is released, an analyst can update the financial model with the click of a button. The updates will refresh the model’s raw data while preserving any additional calculations or formatting made by the analyst.

If the new report contains data that the company had not disclosed earlier, the plugin will notify the analyst so that they can incorporate the new information into their calculations.

Daloopa offers a free version of the plugin that lets users explore financial models for up to five selected companies. The full version comes with a price tag, and users need to contact the startup directly for specific pricing.

There is also a premium offering, Daloopa Plus, which provides real-time access to new financial data as soon as it is released by the company or the SEC.

The startup seems to be targeting large funds, as indicated by the fact that 9 out of the 20 largest shareholders of Disney, 6 out of the 20 largest shareholders of Uber, and 7 out of the 20 largest shareholders of Amazon are among Daloopa’s clients.

Recently, the startup raised $18 million in new investments, bringing the total amount of funding to $41.4 million.

What’s the Gist?

First off, I was impressed by a statement from Daloopa’s founders in a post announcing their latest investment round: "Daloopa is a simple business." They describe the process as cleaning and standardizing publicly available data and storing it in a database, after which they simply update it.

Such an admission about a “simple business” is rare. Most founders are eager to talk about how complex and sophisticated their business is.

The testimonials on Daloopa’s website reflect this simplicity: “Before, I used to spend three days manually entering data and formulas to build a financial model. Now it happens instantly.” “After a new report is released, I no longer need to manually recalculate everything. I click a button, update the data, and can go home for lunch to spend time with my kids.”

This, to me, confirms a point I’ve made before: the most valuable and useful applications of AI right now are not in solving “space-age” problems but in optimizing mundane, routine tasks. These tasks take a lot of time and are prone to human error.

This means that the best AI platform ideas are not necessarily new or revolutionary but focus on automating what people are already doing manually.

But an even more exciting trend is the upcoming rise of AI-to-AI communication, where AI systems will interact with each other directly without the need for human intervention or data transformations.

Take a simple example: On one side, there are AIs that can expand key ideas from an author into a full text. On the other side, there are AIs that help readers condense long texts into key points. The question is, why go through the extra step of converting “key ideas” into full text and back? This transformation process risks distorting the core ideas and their meaning.

The same concept applies to search engines and marketplaces. On the seller’s side, AI tools already optimize SEO pages and improve seller and service provider listings. Meanwhile, AI tools on the buyer’s side help customers sift through search results and product catalogs to find the items they want to purchase.

So, why not create marketplaces where buyer-side AI assistants can directly communicate with seller-side AI assistants? This is something I recently discussed in one of my reviews.

The same applies to finance!

Every proper company has its own financial model that helps them track their operations and achieve certain results. Platforms already exist that use AI to help founders build these financial models. For example, Scaleup Finance (which raised $25.6 million in funding) or Puzzle (which raised $45.3 million in funding).

Then, companies disclose these results publicly or send them to their investors and shareholders. Later, shareholders use AI tools to build financial models from the data and draw conclusions from them. Daloopa is doing this with public company data, while Standard Metrics (which raised $29.5 million in funding) is working with data from portfolio companies of venture funds.

But isn’t this the same as turning key ideas into text and then back?

So why not have AI systems for companies and shareholders directly communicate in the language of financial models?

Or, why not have AI systems from startups and venture funds communicate directly during the investment fundraising process? Or, to generalize this idea, why not let companies communicate directly with AI tools for fundraising, including loans? Financial decisions made based on both data points and the financial model behind those data points would be more balanced and informed.

This approach could be applied not only to banks but also to SaaS platforms and marketplaces, as well as a wide range of borrowers. According to venture fund a16z, fintech has become a new revenue stream for SaaS platforms and marketplaces. SaaS data and transactions processed through marketplaces can give insights into a company’s financial health, allowing investments or loans to be offered when needed. But it could be done more directly — if the AI systems of vertical services can communicate directly with the AI systems responsible for company finances.

Key Takeaways

The first general direction of development is the creation of “simple businesses” that automate manual tasks, which, as Daloopa itself acknowledges, is exactly what they’re doing.

The most suitable areas for automation are those where many people are currently involved in repetitive tasks, where these tasks are time-consuming, prone to error, or where speeding up the process would yield significant benefits. What other “simple” tasks can be automated with AI based on your own experience?

A narrower focus would be automating financial tasks, both in the creation of financial reports and models and in their analysis. These tasks are often tedious and time-consuming, and mistakes can be costly. Speeding up decision-making can lead to substantial profits or avoid major losses.

You can approach this market from various angles or even sequentially. For instance, you could first create a platform similar to Daloopa that helps investors and borrowers analyze reports and build financial models, not just for public companies but also for private ones. Then, you could develop a platform like Scaleup Finance to help more companies create their financial models and reports. Later on, you could create a marketplace where AI tools from these platforms communicate directly with each other, optimizing options for funding and lending.

By taking a bird’s-eye view of any market, we only see financial flows in motion. The finer product details become blurred from that vantage point.

Therefore, any platform for financial analysis is a crucial market that can now reach a new level through AI technologies.

This is still an area where new players can enter. Many startups consider it too boring and unsexy, but for those who understand that financial topics are inherently tied to money, this is an excellent opportunity.

Company info:

Daloopa

• Website: daloopa.com

• Last Round: $18M, May 7, 2024

• Total Investments: $41.4M, 3 Rounds