The landscape of financial statements technology has changed dramatically in 2024 – and it’s about to change even more in 2025.
Handling financials already underpins several job functions, including:
...(to name a few)
Yet, it’s not only financial statements’ critical role in and outside financial services that have inspired technology vendors to build dedicated solutions. Changes in financial statement technology operate in tandem with changes to the role a financial statement plays.. Case in point – financial statements are no longer instruments for record-keeping. Instead, they are useful strategic tools for future planning.
Future planning is an essential job role in ensuring:
Accordingly, businesses are increasingly turning to solutions for stripping and computing the relevant data from financials, essentially converting dense tabular data into actionable insights.
Popular free tools like ChatGPT offer basic commentary abilities for financials. For example, you can upload a financial statement and ask the AI virtual agent questions about it.
Dedicated software as a service (SaaS) offers the following features: extraction, validation, analysis and more. Let’s explore their capabilities further.
We can triangulate the moment that financial statement technology changed to an exact day – November 30th 2022 – ChatGPT’s public release date.
As aforementioned, ChatGPT’s debut had a direct use case for financial statements. Originally, you had to copy and paste the raw financial data into ChatGPT’s user interface, as ChatGPT-3 didn’t offer an upload feature. However, GPT-4 resolved this.
As a Large Language Model (LLM), ChatGPT offers more than raw data extraction – it can deliver a concise contextual commentary about the financial statement.
Of course, we can’t understate the impressiveness of raw data extraction from financials, which has proven a historic headache for data extraction experts (like ourselves). The complex structures and relationships between the line items proved somewhere between challenging and impossible for inflexible data extraction technology.
But once LLMs hit the market, anything seemed possible.
We were one of the first vendors to offer data extraction from quarterly reports. Since then, the financial statement technology landscape has expanded significantly, with numerous vendors vying for market share.
Through a brief analysis, common features of the current financial statements technology market include:
AI is a necessity when reading financial statements. Proprietary, commercial or open-source AI and machine learning algorithms will likely power new financial statements technology. Such software will replace poorly performing, rule-based software.
Instead of queuing reams of documents for processing, new financial statements technology will only take seconds, not hours.
Gone are the days of on-premise installation. Now, cloud-based financial statements technology is generally cloud-based, offering accessible and collaborative workflows to teams worldwide.
SaaS solutions generally offer different modes of cloud-based integration, such as REST API, secure file transfer or work directly on the user interface (UI).
The term ‘financial statements technology’ doesn’t immediately signal a particular use. Here are a few current deployments of these technologies.
If your financials are in PDF format, the data is ‘locked’ and inaccessible for manipulation in Excel. Some financial statement products can quickly convert financial statement PDFs into Excel by extracting the line items and inputting them into a downloadable Excel spreadsheet.
If there’s one thing that analytical AI is known for excelling at, it’s uncovering trends in large datasets. For risk assessment, with processes predicated on historical data (like that contained in financial statements), AI analytics tools might prove invaluable for risk assessment controls like:
The short answer? By being implemented into an intelligent workflow that catches errors at every stage of the financial statement preparation process. Here's how such a workflow might look:
The above example is just a glimpse of how you can deploy AI-powered financial statement technology to prevent errors. The workflow will vary depending on how it is embedded, and, of course, how financial statement technology develops in 2025 and beyond.
Financial statements technology will begin taking over the basic function of a typical analyst by collating, cleaning and reviewing financial data. In turn, analysts will likely have to start evaluating the use of and taking accountability for their AI controls. The Corporate Finance Institute suggests ‘foster[ing] a culture of transparency and accountability’ where employees feel comfortable raising concerns or suggesting improvements for the company’s AI controls.
Providing attestation and assurance likely means reviewing the control environment and conducting governance maturity assessments. Conducting these assessments requires skills that auditors, accountants and analysts use constantly. Therefore, adopting AI won’t (just) require analysts to acquire new digital skills to access AI – it will also involve using their pre-existing skills to make AI work for them.
We’ve said it before – ‘your data, your way’. Instead of outputting raw data from financial statements, financial statements technology will begin generating customised insights, hand in glove with their users.
The financial statements technology landscape used to be limited to relatively rudimentary accounting and payroll software. In 2024, infusing robust technologies with new AI abilities has produced powerful software designed to navigate complex financial data. In the future, we’ll likely see financial statements technology become a more ubiquitous solution within financial services.
Interested in exploring a market-leading financial statement extraction and analysis solution? Try Financial Statements AI for free by booking a demo with our financial data project team or emailing hello@evolution.ai.
