Balance sheets are important storytelling tools. They provide a snapshot of a company’s finances at a given moment so analysts can explore what a company owes and owns. However, examining balance sheets during audits and financial reporting is time-consuming.
In response, fintechs have developed various balance sheet analysis tools designed to alleviate analysts from these mundane back-office tasks. As a result, manual data wrangling and basic ratios will likely be relics of the past by the late 2020s.
Let’s explore what balance sheet analysis software can do, including its features and how it can be deployed.
When Evolution AI opened its doors in 2015, people couldn’t believe that AI was capable of extracting data from financial documents. Fast forward to today, and people’s expectations of AI have caught up – they understand that AI offers a fast and convenient alternative to manual data entry.
AI has made strides in natural language processing (NLP), but extracting data from balance sheets remains a challenge. Two factors contribute to this difficulty: the complex and variable page structure of balance sheets and the inherent difficulty AI faces in interpreting the relationships between line items. Even minor semantic variations, like ‘shareholders' equity’ vs. ‘stakeholders' equity,’ can hinder AI's ability to normalise information across different statements.
Now, machine learning algorithms can demonstrate an astute understanding of what makes balance sheets ‘balance’ the relationship between assets and liabilities, even when the structure and wording of line items change.
Financial ratios are the bread and butter of financial analysis. It may seem surprising that AI tools have taken so long to calculate basic ratios. Unlike older AI, an accountant can (easily) calculate the Debt-to-Equity ratio as a simple division of Total Liabilities / Shareholders' Equity.
Strides in AI's capacity to contextualise the relationships between data points mean that AI tools can replicate human analysts' capacity to calculate simple financial ratios (such as EBITDA and net or pretax income). For more information about what financial ratios AI excels at calculating, check out an overview of Financial Statements AI.
Since accurately predicting a business's financial performance remains challenging for AI, vendors are making significant investments in predictive AI and machine learning. Machine learning talent acquisition will grow by 200% by 2030, and many companies are prioritising the development of sustainable AI infrastructure.
Currently, AI tends to churn out generic advice (i.e., recommending participative management, etc.) when deployed for business strategising. Consequently, firms are investing in training large language models (LLMs) specifically to analyse and generate business strategies from balance sheets.
In the next few years, we expect a rapid influx of prediction and financial modelling software designed to process balance sheets and produce forecasts, recommendations and acute insights.
We’ve condensed the core features of balance sheet analysis software into three categories: collaboration, customisability and compliance.
Different users will require access to necessary data. Check that the balance sheet software you’re researching has distinct user settings that allow different team members to collaborate on financial statement projects.
The requirements of financial analyses will vary, so the balance sheet analysis software you choose should be flexible. For example, you should be able to switch between screens, deploy the platform across different browsers and devices and use it simultaneously with other programs.
Compliance with financial reporting regulations must be built into the software’s nervous system. Firstly, the vendor should demonstrate dedication to data security by producing a relevant certification, such as SOC2 or ISO27001. It should also maintain a clear audit trail and encrypt data in transit.
Balance sheet software should be user-friendly, allowing analysts with varying financial expertise to analyse financial statements.
One key consideration of any balance sheet analysis platform is whether it will play nicely with other financial reporting and audit tools, minimising data silos and promoting consistency across data.
How can you check this? A Proof of Concept (PoC) or trial will indicate whether a balance sheet analysis system will integrate with your IT architecture.
As most major large language models (LLMs) state on their websites, consider manually validating the output of any critical financial information. We’ve written about the potentially disastrous consequences of skipping this step.
Our team of expert machine learning engineers has worked for months developing Financial Statements AI, a balance sheet extraction and calculation tool. It's super straightforward to use: simply upload the balance sheet (or income statement) and download the extracted and calculated data.
Would you like to try Financial Statements AI? Book a demo to find out how you can try Financial Statements AI for free.
