The Finance and Banking industry has strict regulations on data management and processing. To optimize the management of large volumes of data while ensuring accuracy, modern Finance and Banking businesses turn to Optical Character Recognition (OCR) solutions.

Previously, storing information in digital form required significant manual effort and was time-consuming. Employees or customers had to input each data field manually, often leading to errors when entering long fields such as file numbers, invoice codes, or ID numbers. By using OCR technology to automatically recognize text and convert it into searchable, editable, and storable digital characters, companies can streamline a cumbersome, manual process into one that is fast, accurate, and fully automated.

Enhancing the Quality of Big Data with OCR

OCR helps optimize big data models by converting text, paper documents, and images into digital data files that can be read, edited, and searched by computers. The data is then stored in a Big Data repository. The processing and retrieval of valuable information cannot be automated without this digitized data source. For financial and banking institutions, OCR allows them to read and extract data from bank statements, contracts, and other important documents as required by the business.

Currently, on average, a financial or banking institution needs 20 to 25 employees working 8 hours a day to manually convert paper documents into digital format. This manual data entry not only consumes significant time and resources but also carries a high risk of data entry errors. Instead of wasting valuable human resources on this inefficient data entry process, businesses can use OCR to automate the input stage. With a well-designed and properly implemented OCR model, the company will have a "clean" data source right from the beginning, making it easy to edit and synchronize if needed in the future. Moreover, data organized through OCR helps in easy management and reuse within the company’s Big Data repository.

OCR Supports Compliance in Financial and Banking Services

Owning sensitive personal data and financial information, banks and financial companies must comply with legal regulations and undergo rigorous audits. Therefore, these companies need to securely store financial records and maintain consistent document storage. During each audit, manually sifting through thousands of paper documents to retrieve specific information is time-consuming and costly. Storing paper documents is equally inefficient. According to a study by PricewaterhouseCoopers, on average, an organization spends $20 to file a document, about $120 to search for a lost document manually, and $220 to recreate a missing document.

The current document scanning process only converts paper text into images stored on a computer, making it difficult to search, edit, or extract text-based documents. The advanced OCR tool solves this limitation, enabling businesses to easily convert paper documents into digital files that can be searched, edited, and retrieved with just a few clicks. This helps banks and financial institutions save on document storage costs, standardize document storage and management processes, and efficiently present information during audits.

OCR Optimizes Company Resources

Manual data entry in financial and banking institutions is a repetitive task that consumes time and valuable human resources. OCR tools enable businesses to standardize input data processing in a fully automated manner, minimizing labor costs and resource usage. As a result, companies can allocate their resources to more specialized tasks, such as core financial and banking operations. Furthermore, OCR eliminates the need for additional investments in inefficient data management tools or other ineffective data-reading solutions, ensuring that company resources are used for the most optimal outputs.