Optical Character Recognition (OCR) technology, which converts scanned text into editable and searchable data, is a vital tool utilized across industries. Banking, in particular, stands out as a significant beneficiary, leveraging OCR to boost operational efficiency and streamline document processing.
Today, this game-changing technology has evolved into a brilliant innovation revolutionizing banking operations by making them much easier, effortless, more efficient, and effective!
But what makes this technology so revolutionary and what are its use cases in banking operations? Let’s find out more.
OCR and The Bank Are a Perfect Match
You used to think that even a basic banking transaction would take at least 5 to 7 business days just a few years ago, whereas now everything happens instantly. OCR technology plays a very important role in making everything lightning-fast. OCR and other digital tools have paved the way for modern banking, and we can’t thank them enough.
Today’s 25- to 35-year-olds, who used to go to the bank with their parents and find traditional banking tedious, time-consuming, and painful, can now do wonders with just clicks on their smartphones. Before OCR, manual backlogs and mountains of paper transactions slowed everything down. As a result, resources were busy, and the bank lost valuable hours performing simple tasks such as checking or processing a bank statement.
Paving The Way For Modern Banking
Fast forward to 2022, and now you don’t need a designated employee to enter check information into a digital system. Optical character recognition can scan a check from top to bottom and identify parts such as the serial number, personal account number, IFSC code, the amount entered, and signature. After identifying these components, the data is automatically entered into the banking system’s designated account assignment column. The best part is that it will all happen before you read this sentence. However, let’s first grasp this technology’s fundamental idea before discussing how OCR is used in the banking industry.
The Three Pillars of OCR Technology
OCR are based on three basic principles: pre-processing, character recognition, and post-processing.
- Pre-processing: OCR ensures that the scanned document or image is optimized before the all-important machine learning programs start using their character recognition and text identification capabilities. This operation removes errors, enhances the image, and removes distortions to improve recognition accuracy.
- Character Recognition : The first part of this process involves creating a fixed set of data that will be extracted. This technique is called “feature extraction,” It ignores unwanted information and extracts only valuable data before turning it into machine-readable text using ML and deep learning.
- Post-processing: Post-processing is used to correct errors and improve accuracy. Dictionaries protect many OCR programs. This allows the algorithm to check and cross-reference the results using custom glossaries developed for specific industries or applications.
If the banking industry develops quickly, the whole world will develop faster. The simple combination of these three basic principles has greatly contributed to the development of the global financial industry. The perfect blend of Machine learning and efficient AI Services can this technology do wonders for you, you just have to find the best service provider and seal the deal to succeed.
Manual and OCR processing in banks
OCR is more than just quick. Manual processing cannot compete with the speed and accuracy of banks’ OCR-based processing. OCR processing solves all problems, including check processing, statement processing, system security, and operational costs.
Profitability
Statistics show that nearly 35% of financial institutions have increased revenue by 2%–5% after incorporating automation solutions into their banking processes while reducing overall manual processing costs by 70%.
Cloud support
Data obtained from receipt and statement processing can be stored in the cloud functionality of OCR programs. This information is encrypted, and only authorized users can see it.
Reduce paper storage
Built-in cloud storage capabilities reduce paper storage while structuring and organizing retrieved data for easy analysis. It can even reduce data loss.
Batch processing and accuracy
The OCR system can process bank statements and checks in batches. With 99% accuracy, the time required to process documents is significantly reduced. On the other hand, humanized treatment has too many limitations. It is vulnerable to fraud and errors while running very slowly. In general, our entire economy depends on banks, and banks depend on OCR to function properly. Its simplicity, capture, analysis, and automation are what make OCR so appealing. Simple technology is revolutionizing the world in ways many people don’t realize.
Bottomline
Nowadays, the need for AI-powered OCR applications has increased in various industries. Your business can greatly benefit from combining the best ML and computer vision experts to optimize your business applications using OCR.
Author bio:
Vishnu Narayan is a content writer, working at ThinkPalm Technologies, a software & mobile app development services company focusing on technologies like BigData, IoT, and AI services. He is a passionate writer, a tech enthusiast, and an avid reader who tries to tour the globe with a heart that longs to see more sunsets than Netflix!
