McKinsey has predicted that data automation will fulfill 10-25% of bank business processes within the next few years. This will increase the capacity of financial institutions. It will also enable banking employees to focus on more challenging tasks.
Banking institutions would require complex problem-solving and creativity. Extracting text from image software will be at its core.
Today, the scope of OCR in the finance industry, data automation isn’t only about online insurance payments or invoice automation. Instead, it helps leverage artificial intelligence to make informed decisions. Platforms like KlearStack handle extensive data and provide five-star customer support along with delivering personalized notices.
McKinsey also estimates that 75 to 80 percent of transactional operations, such as general accounting operations and payments processing can be automated. To add to this, up to 40 percent of more strategic activities like financial controlling and reporting, analysis, treasury, and financial planning, can be automated.
Coming to our topic of discussion in this blog, let’s understand what OCR is. Also, we will have a look at the business use cases as well as the advantages.
‘OCR’ stands for Optical Character Recognition, commonly known as ‘Text Recognition,’ a popular technique for extracting text from images. An OCR program is a tool that extracts and re-purposes data from scanned documents, camera images, as well as image-only pdf.
An OCR software uses a combination of hardware, such as optical scanners, and software capable of image processing. For text extraction, the OCR tools utilize several machine algorithms for pattern recognition to identify the presence and layout of the text in an image file.
These tools are specifically trained to identify the shapes of characters or numbers on an image to recognize the text. These can reconstruct the extracted text in a machine-readable format, due to which the extracted text can be selected, edited, or copy-pasted like regular text. More straightforwardly, OCR converts digital data in image format into editable word-processing documents. Several software and tools, offline and online, allow OCR technology to extract text from images, KlearStack being one of them!
The primary benefit of OCR technology is that it automates manual and time-consuming data entry tasks.
By using OCR, one can create digital documents that can be edited and stored per requirements.
An OCR tool processes images to identify the text and creates a hidden layer of text behind the image. A computer can easily read this additional layer, making the image recognizable and searchable. This is crucial for industries like banking and insurance as they have to deal with media and documents daily. Here are some of the significant benefits of OCR and natural language understanding to automate the extraction of values from images:
- Faster, automated processing and conversion of paper-based documents into digital formats, which accelerate workflows
- Saves time and reduces the scope of manual errors
- Restricts the need for manual data entry
- Reduces manual data entry, which indicates reduced overall costs for the business
- Saves paper and storage space since more data can be converted to electronic format
A typical example of leveraging OCR and natural language understanding to extract text from image software like KlearStack can be seen in medical insurance claim form processing.
By extract text from image software makes it easy to compare the insurance claim with the policyholder’s details. OCR-equipped systems can flag any anomalies in the data to the concerned departments and prevent possible fraud.
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Business Use cases that extract text from image software:
Now, let’s take a look at business use cases and benefits of leveraging natural language understanding and OCR (optical character recognition) to automate the extract text from image software:
1. Automation of Accounts payable:
Automating accounts payable via leveraging natural language and OCR to extract text from image software enables customizable business rules. This ensures invoice tracking with minimal output and high accuracy in less time! It renders accessible audit trails and serves as a central point of entry for all invoices concerning the accounts payable department.
2. Extraction of text from hospitalization claims:
Medical documents and patient files are arguably the most important documents concerning the insurance sector. The enhanced functionality of software like KlearStack makes it an excellent way for healthcare organizations to have total confidence in the safety of the extraction of text.
Besides, manual handling and copying are time-consuming processes that take up countless valuable working hours. KlearStack can cut down or even eliminate manual entry, saving insurance companies a lot of money. This is helping many insurance companies free up resources to do cognitive tasks.
3. Processing of NACH mandates: For ECS transactions in banks:
Financial institutions use ECS to debit monthly EMIs for loans from the borrower’s bank account. The National Automated Clearing House NACH handles all of the debit processes of ECS transactions.
ECS transactions under NACH mandates require compliance with several documents.
Here, leveraging extract text from image software helps fill in details quickly with less scope for error.
Conclusion: Thus, with KlearStack, clients will be assured of the best financial options and products. And that creative problem-solving and development of new products and services enhance the customer experience. In other words, banks will look and feel much more like tech-based companies with this extracted text from image software.
With KlearStack, you can extract text from image software in varying formats. This is applicable for receipts, legal documents, and more with “human-level accuracy.”
Automate your manual document processing and cut costs by up to 70% with more than a 200% increase in productivity.