Often hundreds of pages long, annual reports contain information about a company’s financial health, performance and prospects. For analysts, the relevant information in an annual report often coexists with pages of irrelevant data.
That’s where data extraction becomes an invaluable asset.
Manual data extraction is one way to target (and capture) the data you need. For example, you might search for keywords in a PDF and copy and paste them into a spreadsheet. Alternatively, various AI-powered data tools that can capture information from annual reports are now available.
Read on to learn more about how AI data extraction solutions work and whether they stack up against human-led data extraction.
At first glance, it may seem difficult for AI to compete with the expertise of a trained accountant or analyst who has years of hands-on experience with annual reports. Traditional financial organisations often perceive manual data extraction as a natural part of the process of analysis.
Also, annual reports generally follow a fixed, easily parsable structure (i.e., a letter from the president/CEO, performance highlights, financial statements, and outlook for future years). Many annual reports contain colourful graphics, which break down the information into digestible highlights, making manual data extraction a (seemingly) simple process for trained professionals.
Compass Group’s 2023 Annual Report combines hard statistics & complex mission statements with appealing visuals.
Therefore, using data capture technology to capture readily available information may seem redundant. However, it’s anything but that. Many analysts are under pressure to find and validate necessary data as quickly and accurately as possible.
The CFA Institute confirms this:
“The pressure on analysts comes from the need to provide accurate, timely recommendations, often under tight deadlines to meet client expectations and investment goals.”
Identifying the relevant data and piecing it into a spreadsheet for strategic analysis can take minutes, if not hours. Manual data extraction is rapidly becoming retro for businesses committed to swift decision-making. Not least because manual decision-making breeds mistakes – often costly ones.
Recall the 1-10-100 rule: verifying the data costs £1; cleaning and correcting the data will cost £10, while a costly error can incur £100. Ultimately, prevention is better than cure: humans are unreliable transcribers, so a more reliable technological solution is necessary.
OCR is not suitable for extracting data from annual reports. Here’s why – OCR is template-based, and there is no such thing as a typical annual report.
Plus, when OCR deals with unfamiliar or variable document types, it outputs data (littered) with errors. If you’re going to the expense and effort of implementing a third-party technology into your existing platform, there’s no point in settling for second-rate data quality.
Accuracy
AI can perform basic summation checks. If those checks fail, it indicates that the data has been incorrectly captured. Also, AI can assign ‘confidence scores’, which indicate the probability that the extracted data is accurate.
In contrast, human operators and traditional extraction technologies lack AI's ability to be self-critical. A bored or tired analyst may not notice a minor error, which will pollute a dataset.
AI won’t let that happen.
Speed
Accuracy aside, speed is another asset of AI-powered technology. In the context of annual reports, AI can extract a spreadsheet’s worth of data in a few seconds. By expediting data extraction, businesses can shave a significant amount of time off the decision-making process.
For example, let’s say that you have three different annual reports, and you plan to extract the net income, operating expenses and current liabilities to compare them. Compiling this data into a spreadsheet would take a human 10 minutes, yet it would take AI 30 seconds or less.
The advantage of speed is that, over time, your employees will spend their efforts analysing and strategising rather than manually handling annual report data. So, in addition to making faster decisions, general productivity will increase exponentially.
Despite its tangible benefits, many companies are slow to adopt newer AI and machine learning technologies. There’s an inherent risk when a company adopts technology that a competitor might develop or adopt a more efficient and cost-effective alternative. Additionally, the chosen technology might become inadequate if you need to extract from a larger volume of documents beyond the algorithms’ capacity.
The most effective strategy to mitigate these risks is to carefully select a scalable and adaptable data extraction solution.
For maximum scalability, the solution should be:
1. Cloud-based
A cloud platform allows you to process large annual reports simultaneously without compromising performance.
2. Integrated via API
A high-performing, secure API will quickly transfer large volumes of data between systems.
The vendor should also demonstrate a clear commitment towards continuously improving their software. Ask the vendor during a demo, “How do you plan to improve your solution over the next year?”.
Hopefully, they’ll provide a well-informed answer. For example, they may plan to reduce processing time or include certain features (i.e., advanced analytics) within their extraction solution.
Though manual data entry offers undeniable convenience with annual reports, you’ll want to consider its scalability. Firms looking to extract large volumes of information from annual reports may benefit from using a data extraction vendor (as opposed to constructing an in-house data capture technology).
Interested in automating data extraction from annual reports? Try Financial Statements AI for yourself - feel free to book a demo with our team. You can also email us at: hello@evolution.ai.
