Conduct Fast, Accurate Analysis With Enhanced Financial Modeling

The past 30 years have been times of unprecedented business growth. Since 2007, the volume of mutual funds has nearly doubled, with total net assets of open-end funds worldwide reaching over $63 trillion. In the corporate sector, profits keep growing over time, and many of the US’s most prominent companies managed to turn a profit even amidst the COVID-19 pandemic.  

Businesses rely on fast and accurate information to inform the investment theses and financial models propelling strategic decision-making to gain a competitive edge and maintain profits. The challenge in creating these models is that the necessary information often lives across disparate systems. Moreover, in the effort to search for actionable insights and accurate information, efficiency and precision can be lost. 

With more and more businesses treating information and data as a corporate asset, fast, reliable, and reputable sources of information have never been more critical to help companies make informed decisions, update financial models, and ultimately grow profits. 

Building financial models

The process of building financial models is relatively simple. First, the summary of a company’s financial performance; a model is based on variable metrics and helps the business forecast future financial performance. 

Financial models built on clean historical data can take hours or days to make. Usually, they require you to copy and paste individual values from 10Ks, 10Qs, and 8Ks into discrete cells in Excel. Not only is this tedious process time-consuming, but it can also lead to data-entry errors impacting your model.

Automating your modeling process

New tech-enabled workflow enhancements to financial modeling have upgraded this once-manual process, automating much of this laborious and error-prone activity. For example, AlphaSense’s Table Tools suite and AI-powered KPI extraction allow you to quickly and accurately export data into Excel and quickly identify quantitative insights hidden within transcripts to help accelerate your modeling process.

Extracting time-series data in seconds

If you’re creating or updating a model, the first place you’re likely to start is a company’s most recent SEC filing. Next, you’ll want to find their income statements, cash-flow, or balance sheets and compare how they’ve changed over time. With Table Extraction, you can select and export time-series data for any table within the filing into one Excel sheet. 

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Auto-extraction empowers you to manipulate this data for your models and saves valuable time in understanding how a company’s performance has changed over time.

Additional data to further your model

Once you’ve taken a look at a company’s SEC filings, you find yourself searching for additional data to improve your model. You can benefit from adding the rich quantitative insights from global filings, ESG reports, and company presentations by clipping and exporting them directly to Excel. 

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Upon clipping a table or chart, AlphaSense parses the object for quantitative data, then organizes the data into a clean data table in Excel, which can be manipulated, reformatted, and redesigned to your convenience, helping you to take your model one step further, without the need to copy and paste.

Extracting KPIs from transcripts

The last resource that is rich with information for modeling is a company’s transcripts. Because AlphaSense’s AI algorithms are trained in the language of business, extracting critical KPIs from text-based documents like earnings transcripts can now be automated at scale. For example, our algorithms parse through earnings calls; they are automatically aggregate and group KPIs like revenue, Capex, and growth. Financial Modeling KPIs 1536x704 1

Not only will our KPI module take you directly to the mentions within one transcript, but you can also easily understand the historical context of how a company speaks about this metric by opening up Snippet Explorer to view all mentions of the KPI over-time, within one single view. In addition, the ability to automatically extract these KPIs from transcripts at scale accelerates meaningful analysis.

In using AlphaSense’s suite of AI and productivity tools, one PE analyst found that not only was he able to save time, but he also strengthened his investment theses.


Check out this video of best practices in leveraging AI to help automate your financial modeling and unlock a free trial of AlphaSense.

Here are a few scenarios and how AlphaSense can help automate your financial modeling process:

What info you’re seeking


How to do it in AlphaSense

Balance sheet Uncover changes in Facebook’s balance sheet QoQ Navigate to Facebook’s SEC filings, find the balance sheet, and use Table Extraction to export QoQ data into Excel.

Product tour for AlphaSense users.

Growth rates Identify growth rates of cannabis hydroponics

Search across broker research for cannabis growth. 

Export the table from a research report showcasing CAGR across hydroponics, cultivation, and vertical farming. 

Product tour for AlphaSense users.

Price Discover changes in Disney’s pricing strategy over-time

Filter by transcripts and go to Disney’s most recent earnings call.

Leverage KPI Module to navigate key snippets related to price, then open Snippet Explorer to see how this strategy has changed over time. 

Add a # into the search bar to uncover mentions with numerical values.

Product tour for AlphaSense users.


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AlphaSense is a market intelligence platform used by the world’s leading companies and financial institutions. Since 2011, our AI-based technology has helped professionals make smarter business decisions by delivering insights from an extensive universe of public and private content—including company filings, event transcripts, news, trade journals, expert calls, broker reports, and equity research. Our platform is trusted by over 2,000 enterprise customers, including a majority of the S&P 100. Headquartered in New York City, AlphaSense employs over 1,000 people across offices in the U.S., U.K., Finland, Germany, and India.

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