In this period of digital advancement, financial institutions are under massive pressure to automate operations. They are looking for a future where manual tasks can be supplemented by using robots. Automation and technology have been the main business drivers for enterprises in several sectors globally. Organizations around the globe are looking for workflow automation by combining robotic process automation (RPA), AI, big data analytics, and OCR.
Automation isn’t only transforming industry operations but it’s also transforming the way enterprises interact with clients. Clients are becoming exposed to digital means and expect a smooth digital experience when connecting with any bank.
Information is high-power energy that helps run your banking operations. When the information isn’t available in the form that your banking system desires. It leads to stuttering efforts to increase productivity and new insights for your organization. If you want to convert your bank or financial institution into a digital platform. Your institution needs to draw out the unstructured documents, documented data, or incompetently embedded pixels in the form of pictures. According to the unit 4 study, “office workers spend around 69 days per year on administration tasks resulting in a yearly efficiency loss of $5 trillion.” Due to this reason, financial institutions should implement artificial intelligence OCR into their system.
Artificial intelligence-based OCR
Artificial intelligence-based OCR mechanisms combine both OCR technology and artificial intelligence to transform a document into a machine-readable and editable digital format. Conventional optical character recognition mechanisms only analyze the document in the form of a picture by detecting based on the design and if the picture contains readable text in a machine-readable format. This helps convert scanned documents into a machine-readable format while transforming the pictures of the available characters to ones kept on its database for conventional OCR modules.
Thanks to unified artificial intelligence, OCR recognizes what it extracts and automatically increases output by machine learning, learning from existing information, and consistently adding missing information, helping to handle and test errors and tangible documents quickly, saving time and cost.
Benefits AI-based OCR provide to the banking industry:
Retrieving of information
An exploring PDF format is one of the use-cases in banking institutions of OCR.
The banking sector utilizes optical character recognition automation to find out phrases, characters, names, and other required data from PDF files. With OCR recognition text verification, scanned documents can be infused into a large-scale data software that is now accessible to read client data from bank statements, contracts, credit/debit cards, and other major contracts.
Significant security with cloud storage
Bank statements, cheques, opening bank account forms, etc, are difficult to save from any unauthorized or illegitimate access. With the aid of cloud storage, it is simple for the organizations to secure it in a systematic way allowing controlled access and securing it from identity thefts.
OCR automation aids in extracting data and securing it on the cloud. It can make it fairly simple for smart gadgets and banking institutions to acquire the data anywhere on any smart device.
Cost reduction
Rather than having staff examine several image documents with an aim to manually feed input into an automated big data processing workflow, banking institutions can make use of OCR to digitize at the first stage of data mining.
Banking institutions can make use of OCR to digitize at the first stage of data mining. By having staff examine several image documents,
Also, the cost of duplicating, transporting, and printing is reduced and the banking institutions can direct all the information in machine form rather than keeping a stack of documents that use large spaces.
Time Optimization
Banking operations save time by using optical character recognition technology in their data extraction processes. It diminishes the operational cost and optimizes the time it takes to finish a task with a better accuracy rate. Therefore, it enhances capacity and increases output generation.
Automation
Banking institutions scan hard copies of bank invoices of their clients or customers after importing them into their software. The extracted data is checked automatically to test its accuracy and originality. The slip will be classified accordingly and moved to their system. In this way, the entire software is digitalized after verifying the content and transferring it to a similar set.
Optical character recognition technology brings convenience to banks such as scanning checks and sending payments with mobile phones. The banking sector only needs to capture the account number, the specified amount, and signature on the check at a high pixel by phone then optical character recognition will take place in the mobile application, and the data is sent to the bank for processing in an efficient way.
Artificial intelligence OCR use cases for banks
Optical character recognition could be the main driver of technology automating industry operations. In the banking sector because of the several types of paper documents still in use across the industry. This automation is most helpful to the banking sector. That has not employed a complete paperless solution for their document processing software or still accept paper documents from clients for the sake of ease.
A financial sector client could satisfy himself with optical character recognition innovation with smart device applications. That permits them to filter checks so as to make a deposit remotely. Banks can utilize optical character recognition to precisely digitize the individual’s personal data on a bank card to be confirmed by a security framework. A few banks in China are utilizing AI-powered OCR. It can also blend with facial recognition programming to give two layers of security at ATMs.
Applications for budgetary administrations, for example, Mastercards can include a high volume of paper reports. Few workers can investigate simultaneously with computerized documents.
Banks can utilize optical character recognition to scan paper documents. Along with several other forms of paperwork that the client may use to transmit responsibility or worthiness.
AI-based OCR can easily recognize new text styles. Which in return alerts the human monitor about the alteration in format and writing style.
The banking sector would then have evidence of revenue on a new client without the need for staff to authenticate each document.
Summing this up
To conclude this, AI-based OCR drivers employ algorithms and techniques that aid organizations. To efficiently embrace digitization with all their data available to them in the digital world. Therefore the banking sector requires a lot of work that is paper-based or requires human labour for data entry. Due to the manual data entry procedure being essential given the sensitive information. Human errors could result in a great financial and reputational loss to the banks. The banking sector could automate the workflow and reduce the chances of undesired errors. The demand for digital solutions by clients is emerging with a variety of online services by online banking.
Data extraction and risk management have become complicated, required, cost-effective, efficient, and error-free at the same time. Artificial intelligence-based OCR is all in one solution package that could smooth the data extraction process with ease.
