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.
We at Docsumo, can do that for you and can save you the trouble of doing everything manually. We not make automation possible, but also ensure accuracy and adaptability. While we work on making our product even better, get in touch with us and sign up for a free trial now!
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.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.