The development of OCR in the banking sector
OCR optical character recognition technology has been around since the 90s of the last century and has quickly been widely used in many important areas all over the world. The bank is one of the industries that uses OCR automation technology the most today. The use of OCR in banks has created a revolutionary digitalization that takes place at a fast and large scale, reducing the load of a large amount of administrative work.
1. So what is OCR?
Optical character recognition (OCR) is computer processing software and allows to convert image files (output images of scanner, camera, PDF file …) into editable documents (word file …). OCR does this through natural language processing technology (NLP), which helps identify letters and characters, and then copies them in the written format and order.
For extracting the required information fields in the papers, such as identity card, driver’s license, … when putting the documents on the system, the user selects and identifies areas to be extracted. After that, the OCR system is identified to convert to text and automatically extract the required information fields.
2. Why in the banking sector, OCR technology needs the integration of AI?
OCR technology has brought a multitude of utilities to the banking industry, but the most notable is document digitization. Representatives of Rabobank (Netherlands) said that their company applied OCR technology into many business activities. Because they need to synthesize and accumulate customer information for themselves, to take advantage of this big data source for many bank projects in the future.
In recent years, with the combination of Artificial Intelligence (AI) and natural language processing technology (NLP), OCR has been expanded into data collection and management technology, playing an immense role in the bank’s business process automation system. Thanks to integrated AI, OCR understands what it extracts and automatically improves output by Machine Learning, learning from existing data and continuously adding missing knowledge, helping to handle and test errors and physical documents quickly, saving time and cost.
For typed documents, OCR gives accuracy of over 98%. Accuracy is a huge problem because small errors can lead to loss of important data. Banks use OCR as a means of transaction security and risk management. Because when using traditional OCR, users can only check documents manually. But when integrated AI and natural language processing technology (NLP), OCR adds the ability to automatically assess risks for any paper document. AI can learn to analyze and process document content thoroughly, detect abnormalities from credit payment information in customer data extracted.
In addition to the most common types of documents that banks often have to deal with such as identity cards (to get customer information), contracts, receipts, bank notes, financial statements, etc., the services such as mortgage and debit cards also have a large volume of paper documents that need to be digitized. The bank uses OCR software to scan customers’ handwritten guarantee documents such as loan paper, power of attorney … The software is able to recognize differences in new text arrangements. From there, send a warning to the supervisor about the change in the format if there is a correction.
Worldwide, many banks use OCR to incorporate facial recognition software to provide 2-layer security at ATMs. Many mobile applications have been launched based on OCR technology to bring convenience to users such as scanning checks and sending money with mobile phones. Customers only need to capture the account number, the amount and the signature on the Czech at high resolution by phone, then OCR will take place in the mobile application and the data is sent to the bank for processing in a fast way.
3. Outstanding advantages of OCR
The digitized documents will be routed through a central data stream, where all experts, analysts and employees in the relevant departments can access and look up information quickly. The application of OCR technology to the system helps the bank manage the work effectively, standardizes the operation of each department, simplifies the processes, reduces the administrative burden, increases labor productivity and optimizes business costs.
Digitizing documents helps the bank save the necessary storage space for the same information, reducing from a few square meters to only a few bytes on the computer, freeing up space, bringing more usable space. Digitized documents can be stored in many formats, convenient for easy searching, editing, simple and sustainable backup over time. In addition, reducing the use of paper helps protect the environment, saving paper production costs.
Currently, in addition to Latin, OCR technology can handle a lot of scripts with a variety of figurative languages such as Arabic, Indian, Chinese … Combined with Unicode standards and translation software ( Google Translate), documents in any language can be scanned, digitized and translated into other languages quickly, helping to reduce the need for human translation, reducing the conversion circle of businesses .
Banking is one of the pioneering industries that goes hand in hand with the development of science and technology. The application of OCR with many advanced scientific platforms has helped banks become smarter and more user-friendly.
Understanding the opportunities and understanding the importance of science and technology, FPT is one of the first corporations integrating the preeminent features of OCR with the AI platform to launch FPT.AI Vision , help the government and businesses solve the problem of digitizing various types of personal papers and extract information from various types of invoices, vouchers and forms quickly, with accurate identification leading the market. Many other artificial intelligence applications of FPT have been completed with many outstanding features, promising to bring a comprehensive solution, simplifying the operation process of many businesses, not only in the field of finance – banking.
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