The Covid 19 pandemic has been the biggest driver for digitization. To provide services, machines need to understand you data stuck inside of documents.
The biggest challenge lies in digitizing data and speeding up the process as the majority of our tasks still happen on paper or PDF files. From getting signature on a report card to filing for a loan there is a huge process of documentation which is highly time-consuming and requires a lot of manual processes.
Companies spend a huge amount of money and resources in managing these documents and keeping track of them. In all the highly regularized sectors like healthcare, banking, legal, supply chain, etc, it becomes even more critical especially during audits. So automating this process becomes of utmost importance.
Document process automation is the design of systems and workflows that assist in the creation of electronic documents. These include logic-based systems that use segments of pre-existing text and/or data to assemble a new document. This process is increasingly used within certain industries to assemble legal documents, contracts, and letters. Automation systems allow companies to minimize data entry, reduce the time spent proof-reading, and reduce the risks associated with human error.
The document process automation is the need for the digital era. A lot of work was happening in this field but recent developments using cutting edge technologies like deep learning and Artificial Intelligence have completely revolutionized this domain.
The earlier approaches were more focused towards extracting features from images using different techniques like edge detection, Gaussian filters, etc which had many limitations in real-world use cases. However, with the enhancement of deep learning models, you do not have to explicitly extract features from the image using any pre-processing techniques, rather you need to train your model using input and output images and your model automatically learns features from those images.
For example: The above algorithm represents the most advanced model that uses Optical Character Recognition (OCR) service to extract the text and layout information, which allows you to work with native digital documents, such as PDFs, and document images (e.g., scanned documents).
Document process automation workflow comprises of following steps:-
The data source is the primary channel of extracting information (data), whether the data is structured, non-structured, or is in any other format. Data Ingestion is the process of reading data through various channels including PDF, Excel, Mails, Word, Scan file etc.
This step requires image and data pre-processing steps like cropping, noise reduction, and filtering which eases the data extraction process.
One of the most critical steps of whole workflow is extracting relevant information. OCR is one of the most advanced technologies and is backed up by different machine learning algorithms. Different computer vision models and libraries like CNN and OpenCV are available which help in detecting and extracting text.
After extracting information and text from the source, classification, or indexing that information according to the template is a major challenge. For instance: while extracting text from invoices, it is vital to differentiate Date, Amount, Name, and other fields from the text you have extracted. Here, Deep learning models come to the rescue that label the data according to its category and automate the whole process.
Now, the information that you extract from the above process could be in different formats and also could be text or an image. Techniques like NLP and computer vision contribute to understanding the underlying data.
The most important step is the verification of data and the quality check. This step can be automated using a template-based approach.
Extracting data from bank statements for reconciling records and comparing them against the company’s own records was manually done via complex spreadsheets.
Claims processing is at the heart of every insurance company. Since customers make claims at a time of misfortune for them, customer experience and speed are critical in claims processing. There are numerous factors that create issues during claims processing such as
Trade finance involves multiple parties coordinating and ensuring the delivery of goods and payments. Banks and companies communicate through letters of credit and other documents that need to be processed.
The processes that have been talked about above can easily be automated.
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In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Processing mortgage loans requires tons of paperwork, followed by a lengthy waiting period for document verification, resulting in a tiresome customer experience. Automation, specifically RPA (robotic process automation), helps you perform these routine tasks more efficiently so underwriters spend more time doing what’s essential. With RPA, enterprises can reduce heavy expenses, fight against fraud and improve customer experience.
Businesses have to process a plethora of digitally typed, printed, or handwritten papers. To deal with it, businesses require efficient and flexible automated document processing solutions that produce accurate results - this is where Intelligent Document Processing can help your business. An IDP solution incorporates the powerful features of Artificial Intelligence and Machine Learning technologies to automate the tasks that once required human intervention, thereby making document processing scalable, robust, and credible.
RPA (robotic process automation) is like a sword that slices through tedious and repetitive tasks in high volumes for your company. Except, it's not a sword - it's tiny robots performing repetitive and routine tasks so that you can focus on core business functionalities. So, whether you're looking to automate the financial auditing statement or you wish to speed up tasks like account receivable and payable, RPA is one of the easiest ways to go about it. You can utilize RPA for plenty of purposes.