Extracting key data points from financial statements and converting them into a structured format is a crucial yet tedious task for finance teams in today’s data-driven business landscape. Companies rely heavily on accurate and timely financial information to make informed decisions, analyze performance, and comply with regulatory requirements. This process, however, is often time-consuming and error-prone, bottlenecking critical financial analysis and decision-making.
By leveraging AI, finance departments can dramatically reduce the time spent on manual data entry and improve the accuracy of extracted information. This means faster financial reporting, more timely insights, and ultimately, better informed business decisions.
Let’s take a look at the different methods of extracting information from financial statements so that you can choose the best method for you.
Manual Data Extraction
Many companies still rely on manual data extraction for processing their financial statements even though there are digital tools to do so. Employees meticulously comb through complex documents like income statements, balance sheets, and cash flow statements. Then, they copy and paste the data into digital systems. Manual data extraction is a time-consuming ordeal that ties up valuable human resources.
It’s also notoriously prone to errors. A misplaced decimal point, a transposed number, or a missed line item can have serious downstream consequences. These challenges aren’t just inconvenient – they can have real impacts on a company’s financial operations. Delayed data extraction can slow down financial reporting, hindering timely decision-making. Errors in extracted data can lead to inaccurate financial analysis, potentially affecting strategic planning and regulatory compliance.
The time-consuming nature of manual extraction, coupled with its inherent risk of errors, highlights the need for a more efficient, accurate, and scalable approach to financial data extraction.
Optical Character Recognition
Optical character recognition (OCR) technology converts scanned documents and images into machine-readable text through pattern matching. OCR identifies individual characters by matching their visual features to the closest known character in its database.
While OCR certainly beats manual data entry with its automation capabilities, it comes with significant challenges that make it an underwhelming extraction solution for complex financial documents.
- Complex formatting and layouts: Financial statements that have complex layouts, tables, and formatting are difficult for OCR tools to parse. This can lead to inaccurate or incomplete data extraction that requires human intervention to sort out.
- Formatting preservation: OCR disregards the original formatting of a document, outputting the extracted text as lines and paragraphs. This means that you’ll lose formatting such as tables and lists, which ultimately adds more work because a human needs to manually format the extracted data.
- Low-resolution documents: OCR doesn’t perform well when identifying text in low quality or distorted scans, significantly reducing its accuracy.
- Selective data extraction: Some OCR solutions simply extract all the text in a document. This is perfect for finance teams that work with every piece of data in financial statements. However, teams that only need to extract specific data fields end up with a lot of irrelevant information to sift through and clean up in order to find the few key figures they actually need.
OCR is certainly a step up from manual data entry, but it’s clear that a more sophisticated extraction solution is needed to accurately handle all types of document structures and provide users with flexibility.
Artificial Intelligence
AI provides the most accurate data extraction out of all the options listed here. This is because AI goes beyond the simple pattern matching of OCR. Instead, it uses advanced algorithms and neural networks to analyze text and understand context. AI models can differentiate between revenue and expenses, recognize different types of financial ratios, and even understand industry-specific jargon. This means it can extract not just numbers, but meaningful insights from financial documents.
AI is the most accurate, efficient, and scalable data extraction solution, as it overcomes the challenges of manual data extraction and OCR in the following ways.
- Speed: AI-powered tools can process large volumes of data in a fraction of the time it would take manual extraction methods. This speed and efficiency are critical in today’s fast-paced business environment, where timely access to financial data can mean the difference between making informed decisions or missing critical opportunities.
- Minimizes errors and ensures data integrity: By using sophisticated algorithms and neural networks, AI solutions can accurately identify and extract relevant information from financial statements, reducing the risk of human error and oversight.
- Flexibility and adaptability: Financial statements can vary significantly in terms of layout, formatting, and complexity, and AI handles this with ease. Because AI isn’t reliant on patterns or templates, it can recognize and adapt to different document structures, tables, and formats, ensuring accurate data extraction regardless of the document’s appearance or structure. Further, AI-powered tools are capable of learning and improving over time. As they process more financial statements of different layouts and formats, they continuously refine their algorithms and improve their ability to extract data accurately, ensuring consistent and reliable performance.
- Scalability: As businesses grow and expand, the volume of financial data they need to process increases exponentially. AI-powered data extraction solutions are highly scalable, enabling organizations to extract data across large volumes of documents simultaneously. This is much more cost- and time-efficient than hiring employees in order to scale. By automating the data extraction process through repeatable workflows, these solutions also drastically reduce the need for manual intervention since they’re able to intelligently alert users when something needs to be manually reviewed. This scalability allows businesses to keep up with the ever-increasing demand for financial data while minimizing the human resources required.
How to Extract Data With AI
Instabase AI Hub combines generative AI and OCR to provide a content activation platform with easy-to-use applications for data and document processing, including financial data extraction. AI Hub uses large language models (LLMs), enabling you to easily work with your documents using natural language. No engineering, technical skills, or model training needed.
AI Hub offers two powerful solutions for AI-powered data extraction from financial statements: Converse and Build.
The Converse app is best for ad-hoc document processing needs, such as when you have one or a few financial statements to extract data from. You can simply type your requests in natural language and Converse will respond accordingly, referencing the original financial statement content for context. For instance, you could type, “Extract the 10 highest grossing product lines.” Converse will then analyze the uploaded document and output the corresponding figures in seconds.
If you need a more automated and scalable approach for processing larger volumes of financial statements, the Build application offers advanced workflow automation capabilities. You can specify the data you want to extract and how the output should be formatted through natural language, streamlining the entire data extraction process and ensuring consistency across multiple financial statements.
How to Use Converse
- Go to aihub.instabase.com. In the upper-right corner, select “Sign in” to log in to your existing Instabase account or select “Get started for free” to create a free account.
- Click on “Create” and then select “Chat” to open Converse.
- Upload a financial statement or folder of statements you need to get information from.
- Enter a query for the information you would like to extract from the document in the text box in the bottom-right corner.
- You’ll see the extracted data in the panel on the right. Hovering your mouse over Converse’s output will bring up a set of icons. Click the overlapping squares to copy the output, and click on the arrow pointing down to download the output as a text file. If you’d like to extract more information, just enter additional prompts in the text box at the bottom of the screen.
How to Use Build
- Go to aihub.instabase.com. In the upper-right corner, select “Sign in” to log in to your existing Instabase account or select “Get started for free” to create a free account.
- Click on “Create.” Then select “App” and “Blank project” to start building a new application in Build.
- Upload a financial statement or a folder of documents you need to extract data from.
- Build is capable of recognizing the visual objects in your documents, including tables and checkboxes. If you would like Build to recognize these objects in your uploaded documents, select them. Otherwise, leave them unchecked. Then proceed by clicking “Upload files.”
- Toward the top of the right-hand panel, select the label icon and then “Create classes” to create a class for financial statements.
- Enter a class name and an optional description. After you enter this information, select “Classify documents” in the bottom-right corner.
- To tell Build exactly what data you’d like to extract, select the “Add field” button.
- Enter the field name that corresponds to the financial data you’d like to extract. You can also choose from a suggested field name.
- You’ll see a preview of the extracted data. If it’s correct, click the “x” in the upper-right corner to save the field. If any of the extracted data is incorrect, you can adjust the results by changing the field type, adding a description, or changing the model used.
- If you need to add additional fields, select “Add field” and repeat as needed until you have created all the fields you want to extract.
- To make this workflow repeatable for batch processing, select “Create app” in the top-right corner and name the workflow. You can also provide an optional description. Then select “Next.”
- Select “Pre-production” if you want to keep the app private or select “Production” to share it with others in your organization. Add optional release notes and then click “Create app.”
- Once the app has been created, select “Open app.”
- Click “Run app.”
- Upload all of the statements that you want to apply this app to. Then click “Run.”
- Once the run is complete, select it from the list to review the results.
- The uploaded statements will populate in the left panel, with the extracted data populated in the right panel. You’ll see the extracted data for each field and any fields where no data was found.
- Select “Export results” in the upper-right corner and select your desired file type. If you need to extract data from financial statements again in the future, no matter their file type, language, or format, you can find your custom app in your Instabase account.
Extract Financial Data With Ease and Accuracy
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