Document Processing in Banking and Finance

The banking and finance sector is all about paper-based, labor-intensive processes. Even with online forms, a large part of data collection still requires paper documents, from account registrations to loan applications. Then, these paper documents will undergo processing which is traditionally conducted manually. This leads to longer waiting lines, errors in verification, lack of standardization, and waste of resources. Another problem faced by the industry is the poorly integrated legacy IT systems. The fragmented and outdated systems are incorporated in an ad-hoc manner, causing process breakage and complexity in tracking. This also makes it hard for institutions to reconcile data across systems.

Banking transactions are one of the biggest markets where intelligent document processing can be used to great effect as these companies have tons of critical documents to manage and query every day. The processing involves document capture, digital imaging, OCR document processing , data validation, data cleansing, indexing and document routing.


The document capture process consists of document scanning to create a digital image followed by document reading using OCR-based technology. In this part of document processing, document images are converted into text which is used to search for relevant information such as amount paid, payee details or other important data. This is done in document imaging which consists of document search, document verification and document retrieval.

Document imaging-focused document processing is also used for other purposes such as checking documents against certain criteria to separate checks from statements and connecting with your platforms to execute check digit validation which helps authentication of documents or indexing which refers to the ability of sorting and storing files in a database by automatically extracting attributes like payee name or document reference number.

For a long time, companies and small businesses have relied on manual invoicing to maintain accounts and process payments. Organizations suffered from dissatisfied and stressed employees, lost or misplaced invoices, reduced productivity, inefficient operations, increased cost and human error, and unreliability due to delayed or missed payments.


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Unlike most OCR engines and data extraction solutions that rely heavily on a set of templates and configurations to capture data, DocDigitizer is based on machine learning that’s capable of capturing and understanding data from any document regardless of the format and pattern. Contact us today if you are looking to implement digital transformation in invoice processing.