Daily, within every industry and across every vertical, more and more manual processes are disappearing as businesses hastily transition to a fully digital version of themselves. While leading the world in so many other respects, the finance industry is lagging behind in the race to ditch paper. That’s because banks face a unique challenge: from in-branch applications for accounts, loans and other services, to cheques and monthly statements, much of their day-to-day remains mired in paper and manual processes. Enter, OCR technology.
Read on to learn how OCR technology is helping banks transform outdated formats into digitized data.
The problem with paper
While cheques, forms and other paper documents have long been the norm within the banking industry, as more processes shift to digital platforms, these legacy systems expose a key limitation within banking.
Consider a situation where you’re:
- Trying to organize client information in an online repository in order to more easily locate and access specific data in the future,
- Seeking to employ automated technologies to more effectively serve customers, or
- Attempting to introduce some other technology to improve day-to-day functions.
In each case, the information needed to power these improvements remains locked within the paper itself.
Scanning might seem like a workable solution, however the traditional way of doing so merely creates a photograph of your document, which is then stored as a TIFF file. While this might mean you don’t have to retain the actual page the words are written on, it doesn’t allow computers to access the content itself. If you had a repository full of documents which had been scanned and you were trying to search for a name or other specific data, your software would only be able to look at the words contained in the file names and metadata.
In banking, regulatory burdens, such as Know Your Customer requirements and recovery and resolution planning, drive further demand for appropriately digitized content as it would simply be too time-consuming to manually handle the volume of information required. A survey by Thomson Reuters found that compliance with Know Your Customer regulations was expected to increase the amount of time required to onboard new customers by an estimated 40 percent over two years, to an average of 24 days. In today’s fast-paced consumer culture, such delays become a liability in any industry that provides customer service.
OCR: Banking’s magic weapon
While traditional scanning methods do not assist with anything beyond physical storage of documents, the banking industry fortunately has a tool at its disposal that supports the key objective of digitization—namely, making data on paper digitally accessible for a variety of functions.
Known as Optical Character Recognition, or OCR, this technology takes scanning one crucial step further: it captures and transforms the content on document pages into digital text format. This allows the content to be digitally accessible and readable so that it may then be used for a variety of functions, from searching within documents to powering machine learning models.
Unlocking the potential of unstructured data within the banking industry
But OCR itself is a means to an end. It’s through the data capture and extraction process that banks can improve efficiencies, automate processes and take the first step toward harnessing their unstructured data. To help demonstrate the benefits of OCR to banking, here’s a look at some of the ways banks are using this much needed technology.
Archiving client-related paperwork, such as onboarding material, is one of the most important uses banks have for OCR technology. Capturing this content in an accessible, searchable digital format helps banks to more effectively store and classify their documents while also ensuring that they are satisfying compliance requirements. But given the potential of automated processes to improve banking, OCR should also be viewed as the precursor to any digital-based strategy or process improvement.
There are numerous applications which hinge on the use of OCR that can help banks to reduce instances of fraud: for example, signature comparison tools can be applied to signed documents to help identify instances of forgery, and machine-learning algorithms can be applied to in-branch applications for credit cards, loans and other services to more effectively spot problematic information. This is a trend banks should explore, as shown by a Moody’s report which found that machine learning models are better than humans at spotting loan applicants who are likely to default. But before any of these promising, risk-reducing processes can be run, the paperwork must first be digitized.
The banking industry is on the forefront of sectors that stand to benefit from digital technologies and innovations. Yet with some traditional banking functions still carried out on paper, the financial industry is limited in its ability to leverage important functions. OCR eases this pain point, unlocking the data in everything from cheques to mortgage applications.