Banks and financial institutions generate huge amounts of data during almost everything they do, and a lot of this still involves physical documentation. For example, checks, application forms, and many other types of paper documents are processed by banks every day, often in massive quantities.
But while these paper documents are still common, they need to be digitized in order to be effectively stored, retrieved, and analyzed. To digitize information, physical documents would traditionally need to either be manually copied by workers or entire documents would need to be scanned by hand. Unfortunately, both of these methods are inefficient, time-consuming, and can lead to a high rate of errors – something that’s unacceptable in an industry where accuracy is crucial to success.
To overcome these challenges, increasing numbers of financial institutions are turning to optical character recognition (OCR) software to automate the transfer of information from physical to digital. While this is useful, basic OCR can also be prone to inaccuracies and can be incompatible with many types of documents. More recently, AI-based OCR is being favored for its improved accuracy and wider applications.
In this article, we explore the different ways that OCR, and particularly AI-based OCR technology, can improve the way banks use data and can help them use big data as part of their operations.
What is OCR for banking?
OCR for banking is essentially the same OCR technology used in any situation but applied to the digitization of banking-related documents. This software ‘reads’ documents and pulls relevant information from them, storing it digitally. Due to the complex and varied nature of financial documents, many organizations are looking to AI-based OCR software as a solution to their data digitization challenges.
Enhanced by AI, OCR technology can offer much higher levels of accuracy, greater comprehension of complicated documents, and a range of other benefits over both manual data transfer and traditional OCR.
How is OCR transforming big data in banking?
Reduced workload for banking staff
Possible the most attractive benefit of OCR software for banks is that it lowers the workload associated with digitizing data and starting to explore the use of big data.
Under normal circumstances, employees would need to manually copy data from documents into a computer, or entire documents would need to be scanned by hand. OCR software bypasses the need for human intervention in the digitization process, freeing up employees’ time to focus on other important business tasks.
OCR automates the digitization process, not only making it simpler to start making the most of big data but also speeding up the process dramatically. Faster digitization means banking companies need to spend less on their big data operations and can start getting results in a shorter period of time. This can make the results of big data analytics more timely, ensuring they have a tangible positive impact on decision-making.
Simpler archiving and retrieval
As the name suggests, big data involves large quantities of information being held for analysis, and it is crucial that there is enough data from which to garner meaningful results. However, this data needs to be organized properly if it is to be retrieved and used effectively.
By optimizing the digitization process, OCR technology facilitates the more efficient archiving and retrieval of large amounts of data, helping banks get the information they want whenever they need it.
Improved data security
Banking and financial documents are almost always highly sensitive in nature, so data security is paramount for all companies involved in banking. But storing data physically is notoriously insecure, harder to keep track of, and there is a genuine possibility of simply misplacing documents.
OCR helps financial businesses to digitize all of their sensitive documents automatically, backing up every document and allowing for the possibility of automated archiving in safe, encrypted storage. This data can then be kept secure and still easily accessed when needed and for analytics purposes.
More accurate data capture
Another huge benefit to automated data capture using AI-powered OCR is the improved accuracy it can offer. This is of particular importance to banking companies and other financial institutions where peoples’ and businesses’ finances are at stake so absolute accuracy is crucial.
Manual data digitization by human employees always leads to some errors, and traditional non-AI OCR can lead to even more errors. However, modern OCR built with advanced AI and machine learning is significantly more accurate, helping businesses ensure a greater level of accuracy whenever digitizing important information.
Digitize any document, however complex
Compared with manual data digitization and basic OCR software, AI-based OCR can comprehend and effectively capture data from a wide range of complex documents. AI-powered OCR almost instantly recognizes a variety of different formats that basic OCR cannot, and that humans take time to understand meaningfully. Additionally, it can be pre-programmed to recognize and take digitize even completely unique and highly complex specific document types. This helps it work seamlessly with any financial business and almost any conceivable document type.
Why choose Digitise.AI for banking document digitization?
Digitise.AI’s transformative digitization platform uses advanced AI to automatically, accurately, and near-instantaneously digitize information from documents, including almost any document associated with the banking industry.
Our experts can also pre-train the machine learning algorithms driving the Digitise.AI platform to understand any specified format of document so you can rely on an exceptional level of accuracy – greater than the average employee or more primitive OCR software.
Get in touch with our team today to see Digitise.AI in action and learn how it could help you eliminate manual data entry forever.
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