How OCR Technology is Transforming Big Data in Banking and Financial Services Industries

How OCR Technology is Transforming Big Data in Banking and Financial Services Industries

The strict compliance regulations and ethics laws of the banking and financial services industries make it necessary for companies to handle documents properly. To optimize the high-volume information pulling of a big data model while ensuring compliance, firms utilize Optical Character Recognition (OCR). Given that a scanned document is simply a digital image of its original paper version, extracting information from scanned documents such as invoices would normally require time-consuming manual effort as the text is not machine readable. By first using OCR technology to recognize the text and convert scanned documents into searchable PDFs, text and data extraction can be automated to make the process much quicker and less labor-intensive.

Enabling Big Data Efficiency with OCR

OCR enables the optimization of big data modeling by converting paper and scanned image documents into machine-readable, searchable PDF files. Processing and retrieving valuable information cannot be automated without first applying OCR in documents where text layers are not already present. With OCR text recognition, scanned documents can be integrated into a big data system that is now able to read client data from bank statements, contracts, and other important documents. Instead of having employees examine countless image documents in an attempt to manually feed inputs into an automated big data processing workflow, organizations can just use OCR to automate at the input stage of data mining.

Using OCR helps enterprises avoid time-consuming and inefficient manual data retrieval, enabling employees to have more time to contribute to the core operations of the firm. Before the Debt Exchange, an international financial organization, began using our accurate OCR software, they relied on 20-25 employees working 8 hours a day to manually convert documents into searchable PDFs. 7 years of utilizing our efficient OCR text recognition technology has enabled the Debt Exchange to optimize their process of document conversion to machine-readable PDFs, saving the company both time and money.

Meeting Regulatory Compliance in Banking and Financial Services

As financial entities that possess PII and sensitive data, banks and financial firms are subject to compliance regulations and evaluation by auditors. Consequently, firms need to efficiently and securely preserve financial records and archive documents. Manually sifting through thousands of paper documents to retrieve specific information is time-consuming and costly, and so is the storage of paper documents. According to research done by PricewaterhouseCoopers, it costs an organization $20 on average to file a single document, roughly $120 to manually search for a misfiled document, and $220 to recreate a lost document.

The time and labor spent trying to find certain content throughout a plethora of documents is time that could otherwise be allocated toward a firm’s core workflow. OCR technology leverages image processing to reliably convert scanned documents from images into searchable PDF files, allowing for specific information retrieval with keyword search. Accordingly, banks and financial organizations are able to save on physical storage units costs and modernize by standardizing paper to digital document conversion.

Additional Benefits of Digitizing Records

Accurate OCR equips enterprises with the ability to standardize their document handling by turning scanned documents into searchable PDFs rather than images saved as PDFs, PNGs, or other unsearchable file types. Using PDFs eliminates a company’s need to have multiple reader software solutions (and the systems in place to pay for, maintain, and train employees on those readers) to access the various different file formats. Furthermore, using PDFs also decreases the amount of employees needed for information retrieval, lowering security risks and saving on labor costs.


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