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Automating Data Extraction From Balance Sheets: A Guide for 2024 [and Beyond]

Miranda Hartley
March 28, 2024

Analysing financial statements by hand is not for the faint of heart. With new AI-powered technologies entering the scene, conducting financial statement extraction for balance sheets is faster and more convenient than ever.

In this article, we’ll discuss the various automation tools available to extract key information from balance sheets and how to manage their potential limitations.

What is a Balance Sheet?

A balance sheet is one of the three primary financial statements, along with the profit and loss (P&L) statement and statement of cash flows. The balance sheet provides a snapshot of a company’s financial health and activities at a specific point in time. Comparing balance sheets from different reporting periods indicates how effectively a company continues to manage its assets, liabilities, and debts.

Manually Extracting From Balance Sheets: An Introduction

Suppose your company has several historical balance sheets uploaded in PDF format. Depending on the ratio or indicator you’re analysing, you’ll need to locate different line items to perform calculations. For example, if you’re calculating:

  • Net working capital: Locate the current assets and liabilities.
  • Fixed asset turnover ratio: Locate the sales and the net fixed assets.
  • Debt-to-total ratio: Locate the debt or borrowings, the reserves and the share capital.

Effective decision-making and financial analysis rely on deriving timely and accurate information. The first step is to capture the data, which employees often complete manually.

Manual data entry – though usually accepted as a tedious necessity in many workplaces – will inevitably produce errors. A study by the University of Oxford showed that errors in manual transcription occurred at a rate of 3.7%.

Moreover, adding an extra digit is the most common manual data entry. These types of errors can be extremely problematic during financial reporting, causing misrepresentation during analysis. 

Let’s imagine, for example, that you’re analysing a balance sheet and notice that the accounts receivables seem overinflated. At first glance, it seems like the company may have failed to disclose a loss (e.g. serious debts or the company may have recognised revenue prematurely). In reality, a human analyst has (inadvertently) added an extra figure.

Such a misrepresentation may lead to severe consequences for investors and other stakeholders. For instance, incorrect data can result in suboptimal strategic planning and investment decisions. Further, inaccurately captured data can cause potentially catastrophic compliance and legal issues.

Luckily, we’re now witnessing an influx of highly effective and usable balance sheet automation tools. Let’s explore what these tools can do.

What are Balance Sheet Automation Tools?

Historically, few automation tools have proven effective for analysing financial statements. The reason? Financial statements are challenging to process. In short, their complex structures and lexical overlaps ensure that poorly calibrated financial technology will fail to process most financial statements accurately.

The image above demonstrates a typical disparity in the structures and languages of balance sheets: on the left, Microsoft, and on the right, Apple.

Fortunately, there is a significant demand for tools to assist today’s analysts in capturing insights from these data-rich documents. In 2024, we enjoy access to a range of automation tools for balance sheets, including automated data extraction.

Recent advances in natural language processing (NLP) have enabled AI to interpret and understand the context of language, greatly improving AI's ability to 'read' balance sheets. A [human] analyst would likely understand that ‘Property, Plant & Equipment’ could be expressed as ‘Fixed Assets’. Now, AI understands it, too.

In the video below, our CEO, Dr. Martin Goodson, discusses the future of financial statement extraction at Fintech Futures’ ‘AI Insights’ event in 2023.

Limitations to Balance Sheet Automation

1. Inferior technology causes more problems than it solves.

Financial technology should empower users, not frustrate them (or their IT team). Getting buy-in from stakeholders is already time-consuming without factoring in challenges with integration and usage. The easiest way to avoid such difficulties is to opt for a specialised data extraction tool (rather than a general automation platform).

2. The transition to automation may cause friction with users.

If not managed carefully, introducing automation may raise employee concerns about obsolescence. Ultimately, while this technology can assist skilled professionals, it cannot replace human judgment. Instead, it can support and improve your employees by removing tedious tasks from their workloads.Discover more about how to introduce and manage AI in your workplace here.


Balance sheets are essential documents for most financial services sectors. With a range of tools available to capture data and analyse the output, choosing the right automation tool for your business will require careful consideration.

Try Financial Statements AI today. You can also book a demo or send us an email at

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