Nowadays, the biggest challenge companies face, despite having an abundance of data, is utilizing this data in a smart way that is most relevant to empower their capabilities to make business decisions. Handling this huge amount of inflowing data becomes challenging for the human workforce. However, advancement in Artificial Intelligence (AI) technology has made it possible today with the AI-based algorithms to scan, read and understand digital and paper documents, just like the humans do. This technology, called Intelligent Data Processing or IDP is, hence, turning quite popular across various industries.
What is Intelligent Document Processing?
Intelligent Document Processing (IDP) is the process of intelligently capturing the domain-specific data across documents and streamline document routing activities using AI-based methods. Regardless of what kind of document needs to be processed, scanned or native PDFs, structured or unstructured, IDP serves a single purpose: to extract structured information without the need to define rules or templates.
Since, the significant portion of information in any organization’s workflow resides in the form of complex documents, it highlights the need for a robust tool that can automate the processing of these documents with minimal manual involvement. This is the reason why intelligent data extraction tools are gaining paramount importance to the organization’s operational success.
According to industry research, more than 80% of data within any organization is unstructured, and most of it is locked in documents. These documents can be in various forms- emails, text, PDF or scanned documents” , which rules-based RPA tools alone cannot process at acceptable precision. To overcome these limitations of RPA, the IDP has recently emerged as a disruptive technology owing to its critical capability to process data without human intervention for large organizations with large volumes of documents. With IDP, the diverse teams are now able to automate data capture, classification and extraction across miscellaneous documents.
What are the different stages in IDP?
Ideally, there are two key considerations before deploying an IDP tool. Firstly, we need to understand the kind of technology we would need to extract the required data from the document. This would largely be determined by the layout and pattern of data in these documents. Structured data would require less advanced tools and its extraction can be easily automated using RPA based automation. On the contrary, unstructured documents throw major challenges in its extraction, and require more sophisticated technology. This is because unstructured and disparate documents prevent the use of regular expressions (Regex) or template matching techniques.
The second stage is the different formats of inflowing data. In a medium or large organization, data is received in different formats such as paper documents, faxes, attachments of emails, scanned PDFs, etc. When organizations integrate IDP into their workflow, unstructured documents can yield organized information with minimal involvement of the technically sound employees, allowing them to focus on more critical aspects of their business operations.
Ideally, an Intelligent Document Processing system is able to recognize, classify and extract distilled information and then route it to the required document workflows for review. So what are the phases to accomplish this Intelligent Processing of Documents?
The initial phase of IDP begins with classifying the type of document being processed. So one type of document can be an electronic document and the other one can be a paper one. This classification is accomplished using the combination of Computer Vision (CV), Natural Language Processing (NLP) and machine learning technology.
Once the document is classified, the next and most significant step in the process is to extract valuable information from the documents. The OCR software recognizes characters and symbols on a document as it scans the images and photographs of documents. Other technologies that enable data extraction are Barcode Recognition, ICR (Intelligent Character Recognition) and OMR (Optical Mark Recognition).
Data extraction involves pre-processing by pattern matching tools like Regex (Regular Expression). Further, it heads to the most critical phase – Contextual text Recognition. Although OCR tools extract the entire free text from the scanned documents or images, they fail to actually understand the document like humans do. Here, AI plays a vital role as the Deep learning models actually perceive the documents and the underlying context before they extract the relevant information. This is the fundamental reason that contributes to ‘intelligence’ in the IDP.
In short, OCR helps in “text extraction” while AI helps in “document interpretation”.
The next step of the intelligent data processing would be to export the structured information using REST APIs, to business processes or workflows automatically to make it available for immediate consumption and can be used by the BI teams in the organization to initiate quick decisions.
Industry Applications of IDP :
A diverse range of industries can benefit from the Intelligent Document Processing (IDP) technology. Here is a rundown of some of the areas in which IDP plays a significant role in improving the productivity of various teams in the organization.
Accounting is one sector that generates quite a heavy amount of paperwork and documents. Documents like invoices, bills, contracts, and receipts which are mostly generated in the paper format result in lesser efficiency and stunted productivity. However, with the help of IDP technology, accounting teams can automatically process the documents and extract key information from those.
What organization achieves is that it shrinks the processing time, eliminates human errors and lessens the processing costs by an average of four times that of the cost of using manual data entry. Besides, with accurate processing of data, the risks of late payments are considerably reduced and companies can capitalize and build their revenue.
2.Foreign Currency Reconciliations:
Automation not only simplifies but also speeds up your reconciliation processes, without compromising on accuracy. IDP can handle smoothly and efficiently the repetitive tasks like transaction matching, thus offering you the capability to drill down on the open entries and exceptions that specifically need additional attention. The result? Spares more quality time for the team to develop strategically important & qualitative activities.
Legal service providers deal with processing a plethora of documents every day, that literary range from archiving and auditing documents, mergers and acquisition-related documents, property filings and following compliance regulations to maintaining customer response times. Majority of these involve complex documentation at each stage. Apart from this, lawyers find themselves referring to volumes of information in the midst of working on a case. The paperwork for their client/s is often handled by an associate manually and hence is prone to discrepancies and errors. Employing an automated system that uses IDP to manage data and documentation maintains security and improves the quality of work.
In the healthcare industry, maintaining records of patients is cardinal. Easy and on-demand access to information can be the need of the hour in the cases of emergency and hence, digitization of all paperwork pertaining to patients becomes critical. When a doctor needs to access a particular file, it becomes a task to scan through all the paperwork in various reports to find what they’re looking for. With the help of IDP, all records and medical diagnostics can be extracted from various reports of patients’ medical history and only related information can be accessed when required.
5.Employee Reimbursement Claims Frauds Detection:
Company employees make fraudulent claims that charge a lot to the company. Manual reviews and audits can miss fictitious claims/expenses, multiple claims made for the same bills. These claims may be part of mistake or intentional fraud, but ultimately organisations suffer in terms of financial impact. IDP benefits in preventing these frauds as it extracts the data from the employee expense documents, drills down through fraudulent entries and highlights the inconsistencies in the claims submitted by the employees. This helps the accounting teams in the organizations to fast-track the disbursements and trim out the disallowed claims. As a result, businesses are saving a significant amount of cash outflows in their ledg
6. Trade finance and consumer durable finance:
With global trade projected to grow, trade finance continues to be a significant pillar of banks’ business model. Although the revenue growth outlook is promising, organizations need first to overcome several challenges to optimizing their trade finance business to maximize profitability. It is especially significant since trade finance is mainly paper-based and labor-intensive. By its very nature, trade finance maintains processes that are highly focused on documentation and checking. The use of paper documents throughout the transaction cycle and across stakeholders is immense.
This usually results in Longer transaction turnaround time, high handling & storage costs , High error rates from manual document verification and Operational risk due to staff turnover . IDP can boost productivity since it deploys intelligent OCR to digitize and turn unstructured documents into readily available data sources and enable faster decision-making and compliance checks through ML and cognitive RPA.
7. Supply Chain Management:
In Supply Chain companies, the commonly faced challenge is invoice processing due to the complexity of semi-structured documents coupled with a lack of extensive intelligent document processing capabilities. IDP can bring in magic here!
Since solutions that adopt IDP can match data from disparate systems and pull it together in a cohesive, understandable way, these solutions are proving to be an excellent fit for end-to-end processes like purchase-to-pay, order-to-cash and record-to-report. Improving processes and eliminating non-value-adding tasks may offer significant savings. Also, a single platform for managing invoices and orders provides a great deal of visibility into the financial supply chain.
Advantages of Intelligent Data Processing
The advantage of automation of business processes is that it eliminates manual intervention in a document-centric workflow. With a single click, data from a document is captured, converted, sorted, indexed and routed to its destination, so it can be stored in a structured format. This improves the overall efficiency and supports the organization to have effective operation in the workflow.
Companies that have adopted the IDP technology into their workflow have observed a significant change in processing time and decreased labor costs by up to fifty percent. Besides, automated data processing enables the previously manual work to be completed in a substantially shorter period of time, thereby saving operational costs that would have otherwise been expensive in case of manual labor.
IDP helps in improving the quality by ruling out any possibility of human error while processing documents. While done manually, information is always stored in an unstructured manner and is difficult to access quickly. With processes that are automated with IDP, then the information is organized in a secure location and is easily accessible as well.
Intelligent Data Processing is a secure technology and maintains data privacy. More often than not, organizations contain data that is sensitive and it is of utmost importance to protect such data from being misused or manipulated. An IDP keeps the information safeguarded by storing it in a secure location accessible only by authorized personnel.
IDP is not specific to any particular process and it can be applied to multiple applications in multiple areas. It does not require any installation, it only serves as a platform for an integrated framework where documents of different formats, sizes, and sources can be scanned and processed. This feature of an IDP system makes it highly scalable and effective.
KlearStack: How does this Intelligent Document Processing platform makes Hyper-automation possible?
KlearStack is an AI-based platform that turns clients’ unstructured documents into a competitive advantage by automating data extraction from various financial documents without human intervention of drawing templates. Processing invoices, resumes, legal contracts, and other documents manually would be inefficient, non-scalable, complex, and might lead to errors.
However, due to Intelligent Document Processing models in KlearStack which are built using OCR, AI, and ML automate these routine processes, improve efficiency, effectiveness, accuracy, and consistency across various workflows in the organization. Learn more about how KlearStack brings artificial intelligence and automation together in this explainer demo video.
It’s high time that organizations across all domains and irrespective of the organization size should adopt such AI based tools to reduce the wastage of resources and leakages in the cash-flow.