OCR (Optical Character Recognition) is not a new word, but if you like to learn more about it, this is the article for you.
Well, it’s a game-changing technology that has now become an ingenious innovation that is revolutionizing banking operations by making them much easier, effortless, more efficient, and more effective!
But what makes this technology a game-changer? Let’s learn more.
The banking or finance industries are the backbone of every country and its respective economies. In recent years 24/7 mobile banking, improved security and fraud detection, blockchain integration, big data analytics, and other digital advancements have led to a major boom in the banking industry.
It is a fact that financial institutions were regularly creating new records and completing transactions for their customers, and we can imagine the magnitude of those records. These ever-growing piles of documents can become a serious concern for banks. Converting all this important data into digital format is the ideal solution to this problem.
Here, Optical Character Recognition (OCR) becomes a miracle solution. It’s a unique technology all banks use as a part of their large-scale data mining. With OCR, banks can now process, monitor, and evaluate data, including large amounts of customer data such as personal and security information.
Moreover, OCR technology and other AI-Integrated tools and apps are now helping banks and other financial organizations to improve their overall performance and profitability.
Let’s dive deep and learn more about this revolutionary technology.
OCR has been around for a while, but organizations have realized its need in recent years. For convenience, it has now become an essential part of the functions of organizations. According to reports, the global OCR market will reach $70 million by the end of 2030.
OCR (Optical Character Recognition) uses technology to distinguish printed or handwritten text characters in digital images from physical documents, e.g., a scanned paper document. The basic process of OCR is to examine the text of a document and translate the characters into code that can be used for data processing. OCR is sometimes also called text recognition.
OCR system is a combination of hardware and software that is used to convert physical documents into machine-readable text. Hardware such as an optical scanner or a special circuit board is used to copy or read text, while software usually handles advanced processing. The software can also leverage artificial intelligence (AI) to implement more sophisticated Intelligent Character Recognition (ICR) methods, such as identifying languages or writing styles.
The OCR process is most commonly used to convert legal or historical paper documents to PDFs. Once entered into this electronic copy, users can edit, format, and search the document as if it were created with a word processor.
There is no doubt that this is the result of technological progress. Also, companies are now taking advantage of OCR technology and providing their customers with a superior experience. In addition, companies are also using this technology for verification and fraud prevention.
Optical character recognition (OCR) converts an image of text into a machine-readable text format. E.g., if you scan a form or receipt, the computer saves the scanned image as an image file.
A simple OCR engine works by storing many different font designs and text images as templates. OCR software uses pattern-matching algorithms to compare text images character by character with its internal database. So, when the system matches text word for word, it is called optical word recognition.
In the past, getting data and converting it into a machine-friendly language was difficult as it had to be done carefully with scope for no errors. Here, the accuracy of work cannot be guaranteed due to human error.
But, with Optical Character Recognition, the scenario changes efficiently, providing accurate results since the AI-based OCR technology extracts data and translates it into machine-friendly language.
Preprocessing is designed to improve the accuracy of the OCR process. OCR understands images as multidimensional arrays. While scanning/capturing document images, there may be some illumination, skewing, and rotation issues; therefore, document images are optimized with techniques such as skewing, normalization, and binarization in preprocessing. OCR extracts text from an input image when using text-to-background technology. It fills out forms using an AI algorithm that recognizes document templates.
After image optimization, data extraction from documents is divided into segmentation and feature extraction. Segmentation uses deep learning neural networks to detect templates and define document segments. After recognition, the software extracts the assets from the document.
For example, parts such as name, date of birth, etc., are identified and filled with appropriate data in an online banking registration form.
Once the data mining is complete, organizations must understand whether the extracted data is correct. Getting the wrong data can be fatal for companies dealing with sensitive information. For example, withdrawing the wrong account number can cause losses to the bank. Therefore, data validation is performed using NLP techniques in the post-processing phase.
The banking sector is prone to financial crimes such as money laundering and account takeover fraud. OCR technology can greatly help the banking industry identify fraudsters as the software checks for unauthorized documents. This technology helps create the most superior software solutions that offer several advantages of optical character recognition for the banking industry. This makes online banking an easy and user-friendly experience for users as well.
The world of technology brings convenience to every industry, and AI-based OCR simplifies the verification process, especially in banking. Employees do not have to spend energy to extract data from each document and verify its legality. Everything depends on the software, and employees monitor all technological inconsistencies. Manual processes can take days to verify a single customer, while OCR can do the job in minutes.
In the manual data acquisition and processing approach, validation takes at least a week. Employees must first satisfy customer requests. It then scans the document and extracts the relevant data. After extraction, it is converted into machine-readable language.
However, AI-based optical character recognition takes the data, converts it into a machine-friendly form, and completes the process in seconds. This allows employees to optimize the inspection time.
In the past, companies would hire a team to test each customer’s intake. In addition, the equipment required for verification is enormous. By implementing artificial intelligence OCR, banks save on equipment rental and purchase expenses significantly. Therefore, OCR is time-saving and cost-effective, especially for the banking industry.
Scammers are easier to deal with because they also use sophisticated methods to fulfill their evil desires. Document forgery is now common, and human verification cannot identify fake documents. So banks need something more rigorous than simple document analysis. This is where optical character recognition comes into play and saves the day for the banking industry. The technology can detect forged documents in seconds, making it easier for banks to filter fraudsters before they cause problems. Furthermore, onboarding legitimate customers is no longer an issue, and the customer experience is not compromised.
Money laundering, account takeover fraud, open banking, virtual currencies, and tax evasion are some of the fraudulent activities that the banking industry faces every year. According to Statista, the UK has closed two-thirds of its bank branches over the past 30 years, the US has closed nearly 9,000 branches, and Europe has closed 6,000 branches due to bank fraud, particularly in digital banking. Fraud prevention is becoming a challenge for all banks around the world. OCR can now be used to fight these crimes. Banks can now recruit legitimate customers, and everyone’s verification means fraudsters aren’t adding to the bank’s headaches.
OCR is not just about speed. The efficiency and accuracy of banks’ OCR-based processing are unmatched by manual processing. Whether it’s check processing, statement processing, system security, or operational costs, OCR processing is the answer.
Answer: OCR stands for “Optical Character Recognition.” It is a technology that recognizes text in a digital image. It is commonly used to recognize text in scanned documents and images. OCR software can be used to convert a physical paper document or image into an accessible electronic version containing text.
Answer: A simple OCR engine stores many font patterns and text images as templates. The OCR software uses pattern-matching algorithms to compare text images character by character against the internal database. This is known as optical word recognition when the system matches text word for word.
The world around us is changing, and with the powerful AI & AI services, we also need to change with the wave. Nowadays, you don’t need a designated employee to enter check information into a digital system. Now OCR Technology is here to help by scanning a check from top to bottom, identifying various parts such as check serial number, personal account number, IFSC code, the amount entered, and signature. After identifying these parts, it automatically enters the data into the specified account assignment column in the banking system. And the best part is, it will all happen before you even read this sentence.