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The Finance – Banking sector is subject to stringent regulatory requirements for data management and processing. To optimize the handling of large volumes of data while ensuring accuracy, modern finance and banking enterprises are turning to Optical Character Recognition (OCR) solutions.

Previously, storing information digitally often required substantial manual effort and time. Employees or customers would have to manually input each field of information, with errors frequently arising in fields like file codes, invoice numbers, and identification numbers. By utilizing OCR technology to automatically recognize text and convert it into searchable, editable, and storable digital characters, businesses can streamline a cumbersome manual process into one that is fast, accurate, and fully automated.

1. Enhancing big data quality with OCR

OCR enables the optimization of big data models by converting text, paper documents, and images into machine-readable, editable, and searchable digital files. This data can then be stored in a Big Data repository. Automating valuable information processing and retrieval would be impossible without this source of digitized data. For finance and banking enterprises, data can now be read and extracted on demand from bank statements, contracts, and other important documents.

Currently, each finance and banking company requires an average of 20–25 employees working eight hours a day to manually digitize paper documents. This manual data entry process is time-consuming, resource-intensive, and highly prone to data entry errors. Instead of dedicating valuable human resources to this inefficient data entry process, companies can use OCR to automate the initial data input stage. With a properly designed and implemented OCR model, businesses can secure "clean" data from the start, which can be easily edited and synchronized as needed in the future. Moreover, organized and systematized data makes it easy to manage and reuse within the company’s Big Data repository.

2. OCR supports compliance in financial and banking services

Since banks and financial companies handle sensitive personal and financial data, they must adhere to stringent legal regulations and rigorous audit evaluations. As such, these companies need to effectively and securely preserve financial records and maintain a consistent document storage system. During audits, manually sorting through thousands of paper documents to retrieve specific information is time-consuming and costly, as is the storage of paper documents. According to research conducted by PricewaterhouseCoopers, an organization spends an average of $20 to file a document, about $120 to manually locate a misplaced document, and $220 to recreate a lost document.

The current document scanning process is limited to converting paper documents into image files stored on a computer, which cannot be searched, edited, or extracted as digital text. To address this limitation, advanced OCR tools allow businesses to convert text and documents from hard copies into digital files that can be easily searched and edited with just a few clicks. This enables banks and financial institutions to save on document storage costs, standardize document storage and management processes, and present information efficiently during audits.

3. OCR optimizes enterprise resources

Manual data entry work in finance and banking enterprises is repetitive, time-consuming, and demands valuable human resources. OCR provides companies with the ability to standardize the processing of input data automatically, reducing labor costs and material resources. Enterprises can then allocate resources to high-value, specialized tasks within their core financial and banking operations. Furthermore, OCR allows businesses to avoid additional costs related to inefficient data management tools and systems, ensuring that enterprise resources are dedicated to the most productive outcomes.

Author: FTECH AI