Financial statements have frequently posed a challenge for companies looking to extract clean, structured data. The complex structure of financial statements often forces companies to resort to manual data extraction. However, manual data extraction wastes time for skilled employees and comprises a long-term drain on company resources.
Extracting data from financial statements is a complex task due to their lengthy nature, sometimes spanning up to 200 pages. Capturing the required values from a financial statement is considerably more challenging than extracting them from a one-page invoice or bank statement.
Traditional data extraction technologies struggle with the intricate structure of each page, often containing complex tables containing rich financial data. Additionally, key information on financial statements may require calculations based on line items.
For example, if you were searching the document for operating income and it wasn’t explicitly stated in the document, you would need to deduct the operating expenses from the operating revenue. While judging these types of calculations pose no challenge to a human analyst, replicating these abilities with machine learning technologies was impossible until recently.
In 2023, dubbed ‘the year of AI’ by the media, there are multiple high-quality AI-powered data extraction tools for complex documents. In this article, we break down how these tools work and how they can be used to extract data from financial statements seamlessly.
AI-powered IDP (intelligent document processing) software is an ideal solution for large volumes of financial statements. When the user uploads batches of financial statements, the software retrieves the requested data fields and outputs them into a structured format.
What makes this approach ‘intelligent’ is that the technology actively understands what each data point means in the context of the document. For example, if you wanted to extract the ‘net profit’ from a financial statement, traditional data extraction software would struggle to distinguish the value from the other values potentially on the page: revenue, cash flow, return on assets and so forth. IDP can harness AI technology to understand complex financial terminology and find the corresponding values on the page.
For financial statements, this is an invaluable attribute: non-standardised financial terminology across financial statements (e.g. ‘Revenue’ can be phrased as ‘Sales,’ ‘Net Sales,’ or ‘Turnover’) means that a deep understanding of semantics is required to identify and extract the data points correctly.
After the data points have been located and validated, they can be exported in a structured format, such as an Excel spreadsheet or a CSV file.
For example, you may opt for straightforward integration via REST API, or a lightweight solution such as uploading documents straight to the interface of the IDP solution. There are multiple no-code solutions like Workato on the market, which can build an automated workflow - i.e. a way of smoothly inputting financial statements and receiving the outputs in a repository of your choice.
Some IDP vendors offer subscription options; some offer a price per page. When comparing IDP vendors, it’s a good idea to have an idea of how many financial statements will require processing per month or year. It’s also worth breaking down each vendor’s guarantees: if they can deliver complete accuracy, a minimum processing time per financial statement, and so on.
Once implemented, IDP solutions prove to be highly cost-effective by minimising wasted employee time and reducing expensive errors.
The duration of training varies based on the vendor and document type. Certain vendors provide AI models with zero-shot learning capability, eliminating the need for training documents when extracting information from documents like invoices and bank statements.
For certain vendors and more complex document types, approximately 200 examples are required before the model can automate data extraction. The training process can normally be completed within 2 to 5 days.
Contemporary AI software for financial statements not only provides fast results but also supports post-processing rules. Some examples of valuable post-processing rules include normalising currency values, standardising date formats, and flagging errors for manual review. These modifications ensure that the extracted data is not only delivered but also presented in a format that immediately enhances its value.
Though financial statements have been historically challenging to process accurately, there are now high-quality options on the market for data automation. Once successfully implemented, data extraction from financial statements is time- and cost-effective.
If you’d like to try Financial Statements AI, book a demo with one of our financial data project managers. Alternatively, email hello@evolution.ai for more information.
Discover more about automated data extraction from financial statements:
Our solution to financial statements
How to extract financial data from PDFs
