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How to Extract Data from Financial Statements

March 25, 2024

A common problem for businesses is the increasing volume and interpretations of financial data. Extracting this financial data from PDF documents, such as balance sheets, bank statements, quarterly & annual reports, and more, is arguably the most time-consuming challenge.

By adopting financial automation software, companies ensure their financial process is efficient, accurate, and timely. Without these solutions, extracting data from financial statements is error-prone and time-consuming. 

How do businesses traditionally extract data from financial statements?

The two most common ways businesses extract data from financial statements are manually or using traditional optical character recognition (OCR). Both of these methods are outdated. 

✍ Manually extracting data from financial statements

Firstly, without automation support, employees manually extract copious amounts and varieties of data. This is incredibly time-consuming and has an increased chance of human error. Plus, it’s unnecessary in the 21st century. Would you do complex mathematics without a Casio? No chance. It’s the same when extracting data from financial statements.

Did you know that BDO increased efficiency by 3x by replacing manual work in their financial statement procedures with DataSnipper? 

🤖 Using standard OCR for financial statements

Though it's not always true automation, one innovation that is widely used for large-scale financial reporting tasks is standard OCR. For a while (and still for many) it's the go-to solution.

However, standard OCR is typically rigid. It often relies on standardized templates and lacks the flexibility of true automation solutions. And, as we know, financial statements come in all shapes and sizes. Plus, the complexity of financial statements continually increases and standard generalized OCR struggles to keep up with these changes.

Financial teams have been ill-equipped with mediocre solutions for far too long. It's time to give them their Casio. But where to start

How should businesses extract data from financial statements?

Three words - intelligent data extraction. It’s a bit of a blanket term with lots of examples but the fundamental goal is the same - true automation.

The best examples combine OCR with intelligent automation features including cross-referencing, document matching, smart search & snip features, and more. They can extract data from PDFs and other complex documentation at scale. As you imagine, this is a huge time-saver.

Many leading names within the audit and financial space have already adopted intelligent data extraction in their workflow. This has resulted in more efficient processes, significant cost and time reductions, and happier employees and clients. 

🤔 Choosing the right intelligent data extraction solution

It’s a problem as old as time. You’ve decided you want to adopt intelligent data extraction. However, after stepping into the intelligent data extraction store, you’re faced with countless solutions. Where to begin?

The hardest thing about adopting a new solution is the onboarding process. You need to learn how to use the solution and then work out how to convince and onboard the rest of your team.

The best way to convince and onboard your team is to show them an intelligent data solution that is simple to use, true automation, fast, and improves the overall quality of service.

For example:

“DataSnipper’s Financial Statement Suite provides major efficiency improvements and is offering features that truly support our teams in analyzing financial statements at scale.”.
Marco de Kruijk, Senior Manager, BDO

Using DataSnipper’s Financial Statement Suite

Your team wastes valuable time using calculators, manually searching for errors, and comparing financial statements. It’s time to step into the future with next-gen “tick and tie”.

Use DataSnipper’s Financial Statement Suite to verify mathematical accuracy, verify internal consistency, check previous-year consistency, and more.

If you’d like to learn more about how DataSnipper helps BDO and other important names with their financial statement procedures, book a demo today. 

Extracting data from financial statements is a pain in the… you know. But it doesn’t have to be.


How do you extract data from an annual report?

Extracting data from an annual report involves a systematic process to ensure accuracy and efficiency. Here’s a step-by-step guide:

  1. Identify the Annual Report: Obtain the annual report from the company's website or investor relations portal.
  2. Understand the Structure: Familiarize yourself with the sections of the report, including financial statements, CEO letter, MD&A, footnotes, and auditor's report.
  3. Focus on Financial Statements: Prioritize extracting data from financial statements such as the balance sheet, income statement, cash flow statement, and statement of changes in equity.
  4. Use OCR Tools: Convert the report into a text format using Optical Character Recognition (OCR) tools if it's in PDF format.
  5. Manually Extract Data: Copy relevant information into a spreadsheet or database, especially if OCR isn't feasible or for non-standard data.
  6. Utilize Financial Analysis Software: Consider using specialized software to automate data extraction from financial statements.
  7. Identify Key Metrics: Extract key financial metrics like revenue, net income, EPS, operating cash flow, and debt levels.
  8. Verify Accuracy: Cross-check extracted data with other reliable sources such as financial databases or regulatory filings.
  9. Analyze the Data: Analyze the extracted data to gain insights into the company's financial performance and trends.
  10. Monitor Changes: Keep track of changes in the company's financial data over time by comparing data from multiple annual reports.

By following these steps diligently, you can effectively extract and analyze data from annual reports for various purposes including investment research and financial analysis.

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