A business’s financial data holds huge value for both daily financial tasks and long-term concerns. Things like financial analysis, checking up on regulatory compliance, and even being able to pay bills on time all require important financial information to be made available for processing.
That’s why financial data extraction is so important in a business’s workflow. It makes all that data available for proper processing so that businesses can make more informed financial decisions and operate with full integrity and compliance.
In this article, we’ll look at some of the key issues around financial data, why traditional methods of extraction are holding organizations back, and how automated financial data extraction software like we offer at FormX can provide a more efficient, reliable solution. We’ll also break down how AI and IDP technology function in this and the benefits of using it to streamline the extraction of financial information.
Financial data extraction is the process of extracting key financial information from documents and making them available for use in centralized systems. When you add in automation to that process, the extraction is done using AI-powered technology such as OCR, ML, and LLM.
This technology can understand the content of financial documents, identify relevant data fields, extract them, and convert them from unstructured or semi-structured formats into organized data which can then be used in analytics, detailing, and other financial tasks.
Extracting financial data accurately and timeously is crucial as it enables businesses, investors, and institutions to make informed decisions and avoid costly delays or errors. It improves risk assessment, facilitates strategic planning, and ensures that any other workflows or business goals that depend on that data can happen without a hitch.
Financial data is the center point of all financial activity in most modern businesses, but nothing much can happen if the data isn’t extracted properly and made available in the right formats. That’s why financial data extraction is being valued so much more, with the industry’s global market jumping from 2.70 billion USD to a projected 5.93 billion by the end of 2029.
Before diving deeper into financial data extraction, it’s important to lay out what data we’re talking about exactly and why it’s so important to compile for a company.
Financial data is any piece of information that helps establish a clear picture of an organization's financial condition. In its raw form, the data doesn’t necessarily hold much value. It’s when it’s processed and analyzed further that it can show trends, aid in strategic planning, and provide insights into an entity's financial performance.
Major figureheads in business such as managers, regulators, and investors also rely on these data sets for various objectives. For instance, when determining whether to invest in a company and monitoring its success afterward.
Internal management teams, on the other hand, prefer to use financial data analysis to assess business performance and gauge the effectiveness of their strategy. Either way, crucial tasks and jobs ultimately depend on accurate financial data extraction.
Though financial data can encompass a wide range of information, these are the key documents that businesses tend to source it from:
- Income Statements: Sometimes referred to as a profit and loss statement (P&L), an income statement summarizes a company's financial performance during a specific period, focusing on revenue, expenses, and profits or loss.
- A Balance Sheet: This is an integral accounting document and financial statement that reports what an entity owns (assets), owes (liabilities), and what shareholders' equity is. It showcases the company's financial health and solvency.
- Cash Flow Statement: This statement summarizes how much cash or cash equivalents are transferred in and out of a business. Money received represents inflows, while money spent represents outflows.
- Investment Data: Investment data refers to the information concerning a person or company’s investment(s) made in stocks, bonds, real estate, and other financial instruments.
- Bank Statements: A staple for any business, a bank statement summarizes all account transactions on a monthly basis in paper or digital form and contains information about checking and savings accounts, such as account numbers as well as deposits and withdrawals.
- Payroll Records: Payroll records are the combined information regarding how much money has been paid or is owed to employees and how this has been calculated. For example, hours worked, contributions to any benefits, tax findings, etc.
Other financial documents, including invoices, bills, credit reports, and more, also provide vital financial information that needs to be extracted. There is almost no end to the documents that financial data can be sourced from, which is why extracting and compiling data can be such a frustrating task when performed manually – something we’ll look at more in the next section.
Precise financial decisions depend on data being accurate and reliable and as such, that the extraction process be free of errors and inconsistencies. Unfortunately, the main limitation of manual financial data extraction is that not only is it resource-intensive, but it’s far more likely to incur duplicates and mistakes than automated approaches simply because of human error.
This is made even more frustrating by how expensive the time and labor manual financial data extraction requires. It’s a fundamentally inefficient approach that involves people spending hours manually entering data into a system or database and as data requirements increase, also becomes difficult to scale up due to high labor costs.
Most manual data entry setups struggle to adapt to sudden increases in workload and can easily be slowed down by a single error, bottlenecking the entire data entry process and consequently, slowing down multiple other business processes. In a research study conducted by Doug Henschen at Constellation Research, it was highlighted that organizations that don’t use a centralized data platform will lose as much as 20% of the time meant for “planning and analysis” to simply collecting data and lose another 30% of their time validating all that information afterward.
This illustrates just how inefficient manual financial data extraction can become and the ways in which it can steal precious time and resources from other areas of a business.
The reason for needing financial data extraction in the first place is that most analytics software can’t process information from unstructured formats such as PDFs or scanned images. Information must be extracted from PDFs and paper documents and converted to structured formats such as JSON or CSV in order for the full value of the data to be accessed.
Intelligent Document Processing (IDP) solutions offer a way to perform this task automatically, replacing the need for manual data entry and all the frustrations that come with it. It uses artificial intelligence (AI) technology such as Optical Character Recognition (OCR) which scans text within a PDF or image and extracts it, and then machine learning (ML) and natural language processing to understand the value of each piece of data so that only the relevant information is added to the central data system.
It's a complex set of technologies, but with IDP, it all works in harmony to extract data from financial statements and other documents without any human intervention needed.
Integrating all this cutting-edge technology and financial data extraction software is much simpler than many expect, and the benefits certainly open a realm of strategic possibilities.
Here are some of the main advantages of switching from a manual financial data extraction approach to an automated one:
Minimize Hefty Expenses
As we’ve discussed, manual financial data extraction can be expensive for two main reasons: labor costs and the expense incurred by data entry errors. Automation addresses both those issues to streamline the data collection process and free up employee time to focus on tasks that need them more.
Financial data extraction software also has a far lower rate of error than manual equivalents which saves organizations significant money in the long run. For example, financial report data extraction, if done incorrectly, can mean that a business stumbles in terms of compliance and ends up having to pay an otherwise avoidable fine. Automation makes issues like this a thing of the past.
Elevate Scalability and Amplify Data Processing Volume
When it’s software you’re working with rather than people, scaling work volumes no longer becomes a costly hiring problem. Technology like FormX’s OCR financial statements ensures that no matter the number of statements a business has to process, speed and efficiency isn’t compromised.
Automation already makes financial data extraction faster, but the fact that this isn’t diminished by the volume of data that needs to be processed makes it even more valuable for business activities. It also does this without the need for human intervention, and with FormX, doesn’t require coding skills either.
Enhanced Overall Effectiveness
The speed, lowered need for labor resources, and improved accuracy and consistency that automated financial data extraction provides means that it not only streamlines financial data handling, but has a knock-on effect on an organization’s overall efficiency.
It ensures that managers aren’t wasting time waiting for someone to extract data from financial statements or checking errors in financial report data extraction. The work is done quickly and accurately so that all other financial tasks, from analytics to basic payments, happen without delay or added cost.
Our platform at FormX provides a powerful set of tools designed to easily convert financial or PDF data to formats such as CSV, Excel, and JSON so that further financial processing can happen quickly and accurately.
Leveraging powerful IDP capabilities, FormX’s financial data extraction software offers a user-friendly approach that allows any organization to take advantage of automation technology and its benefits – from improving scalability and efficiency to simultaneously minimizing operational costs. No matter how much data you’re trying to process, we can help.
There are several FormX data extractors ready to use, but the platform also allows you to train your own ML models to suit your specific needs, whether dealing with payroll records, balance sheets, or income statements.
Sign up for a free trial or schedule a demo with our experts today to start your digital transformation journey with FormX and optimize your approach to financial data extraction.