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Approaches to Financial Statement Extraction in 2024

Miranda Hartley
October 23, 2023

Lengthy, dense and full of complex tables, financial statements previously resisted quick and easy data extraction. In this article, we’ll discuss the pros and cons of each data extraction method in 2023, from manual extraction to legacy optical character recognition (OCR) software to intelligent document processing (IDP).

Manual data extraction

Many companies rely on manual data extraction to convert information from documents into actionable formats. A study showed that 40% of employees spend up to a quarter of the week completing manual and repetitive tasks that could be automated, such as manual data extraction.

Manual data extraction methods can vary – from having a team of manual-data entry clerks, to using highly-trained employees to transfer data from an unstructured to a structured format.

Transforming the information from financial statements into an actionable format is often considered part of analysis. However, this is operationally inefficient, especially when we can train contemporary technologies to extract specific data from financial documents. Let’s examine one of these technologies—OCR.


Optical Character Recognition (OCR) was a revolutionary technology when first introduced around a century ago. An OCR-based workflow consists of the following:

1. The user uploads documents.

2. The OCR technology then scans the document by matching the contents of the image with preformatted templates.

3. Finally, the OCR outputs the data.

In theory, OCR should be significantly faster and more affordable than using valuable employees. In practice, however, standard OCR is rigid and inflexible.

While highly useful for information in a static format, such as cheques, IDs and multiple-choice exams, OCR is unsuitable for financial statements. Training an OCR engine to extract data from one financial statement won’t teach the technology to extract from another.

Similar to manual extraction, OCR can lead to costly errors. For instance, financial statements contain a wealth of information about the financial health of businesses–meaning errors in data capture can be catastrophic.

All it takes is one bored employee or one piece of outdated tech to confuse liquidity for liability or equity for equities. The end result? Potentially massive problems down the line.

AI-led automation, or IDP

Intelligent document processing (IDP) combines AI with OCR to form a holistic and robust automation solution. Automated data extraction is more than the sum of its parts, however. When deployed effectively, IDP is a versatile and user-friendly back-office tech.

The benefits of intelligent document processing include the time-to-data (usually a few seconds) and the costs. IDP is cost-friendly and also priced transparently.

To see this kind of technology in action, try our intelligent document processing platform, Evolution Transcribe.


For years, OCR was the tried and true method of data extraction. During this time, AI-powered OCR alternatives were merely OCR models with extra rules added. However, in recent years, AI has significantly evolved in its sophistication as well as its commercial applications.

Any enterprise AI solution should save time, but OCR can actually cost you more time than it saves by producing incorrect information. For instance, when highly-trained employees spend time combing through outputted data for mistakes or missing information, at that point, the technology becomes obsolete. In this instance, manual data extraction becomes preferable.

Many IDP providers also offer 'managed services' – a type of all-in-one solution where you can submit a combination of different document types (such as financial statements and bank statements, etc.). Under managed services, someone will validate the data – a system known as 'human-in-the-loop' (HITL).

Another benefit of managed intelligent data capture for financial statements is that enhancing the technology is straightforward. Let’s take a look at three of the top enhancements:

  1. Forecasting market trends such as creating projections and assessing how economic conditions may affect the financial performance of the company in question.
  2. Visualising data and performing calculations: such as calculating the debt-to-equity ratio by dividing total liabilities by total shareholder equity. A quick task that allows immediate insight into the captured data.
  1. Enriching data through third-party information: such as competitor data or economic indicators.

Another notable point in the IDP vs. OCR debate is the ease of implementation. In fact, both technologies can be straightforward to integrate. Many document processing platforms can be accessed in multiple ways, from simply uploading documents directly or using an API key.

In sum, when it comes to data extraction from financial statements, OCR simply cannot compete. While manual data extraction is a tried-and-tested method of data capture, it also adds unnecessary time and cost to the financial reporting process. In fact, until a few years ago, using technology to do the heavy lifting of financial statement extraction would have been seen as a distant prospect. Now, in 2023, the technology is at its prime and in high demand.

Exploring Intelligent Data Extraction With Evolution AI

Evolution AI has a long track record of successfully automating data extraction from financial statements. Our generative AI models are trained to extract specific information from financial statements quickly and cost-effectively.

To discuss your business’s use case, our team at Evolution AI invites you to book a demo with one of our experts today.

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