The financial services industry relies on data to drive decision-making and invests heavily in IT technology to help in that effort. But finance teams hit a roadblock when it comes to effectively pulling data from forms such as SEC quarterly 10-Q and annual 10-K forms because, till now, they lacked an effective way to automate the processing of unstructured data.
Forms like 10-Q and 10-Ks contain valuable data on public companies, but they consist mainly of free-form text and numbers. That makes it all but impossible to use automated document processing tools that require templates to define where relevant data is in each form – because no two 10-Qs or 10-Ks are alike. (For the sake of simplicity, from here on, we’ll refer to 10-Q forms, but the same applies to 10-Ks.)
Manual financial form processing
But the forms contain data that financial services firms need to track, such as any material change to a business, income and expense levels, net gains and losses, gross margins, and more. The forms tend to come out together in droves, to make matters worse, coinciding with each company’s fiscal quarter- or year-end.
So, financial firms need small armies of analysts to read over the forms, find the essential bits of data, and manually enter the data into a spreadsheet or database. At that point, the data is in a structured form, where it can be fed into a downstream platform that performs analytics, comparisons, and whatever else the financial analysts need. But financial firms are naturally interested in automating more of that up-front part of the process.
Related content: Intelligent document processing & intelligent process automation for financial services
Intelligent Document Processing
That’s where intelligent document processing (IDP) helps. A good IDP solution will “read” 10-Q and other financial documents just like those analysts do and find the data that needs to be extracted. In short, they help achieve true financial services automation.
These solutions take advantage of optical character recognition (OCR) along with artificial intelligence technologies, including machine learning, transfer learning, and natural language processing. Our IDP is built on a massive reservoir of labeled data points, enough to provide the context behind most any document.
A robust intelligent automation platform will also come with tools that make it simple for financial analysts to label actual 10-Q forms, telling the tool which data to extract. That’s an important point: it’s the folks on the front lines, those tasked with reading the financial forms and extracting data, who build the automation models for investment banks and others.. No IT or data science expertise is required. Giving these so-called “citizen data scientists” the ability to build models enables financial firms to scale their automation capabilities quickly.
Related Content: How Chatham Financial Increased Process Capacity by 300%
Automating financial document processing
So, here’s how that financial document analysis process works when an IDP solution is involved.
The analyst takes maybe 200 10-Q documents and spends a few hours with the IDP tool, labeling the sorts of data that need to be extracted, including material change clauses, gains, losses, and more. At that point, the model should have enough information to accurately “read” any 10-Q and be able to find those fields no matter where they appear in the document.
You’ll want an IDP solution with analyzing capabilities that extracts the relevant data from each document and inputs it into a spreadsheet or database. In minutes, the tool can process documents that would take humans many hours. What’s more, computers don’t get bored or tired, so it’s likely to produce far more accurate results as well.
Related content: Gartner 2022 Market Guide for intelligent document processing solutions
Choosing the right Intelligent Document Processing solution
Of course, the automated process will only be as effective as the IDP solution you choose. Our Unstructured Data Platform is built on a database of some 500 million labeled data points. The use of transfer learning and an intuitive user interface makes it easy for users to build models that put that database to work on their specific task. A citizen data scientist can build a model that will work with about 95% accuracy in an afternoon.
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