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    Intelligent Document Processing: Everything You Need To Know

    Intelligent Document Processing: Everything You Need To Know

    How much staff time does your operation spend processing documents? Most companies devote extensive human resources to this task:

    • One major logistics company required more than a dozen employees to review and verify documents varying from bills of lading to letters of credit.
    • A leading specialty healthcare company had over 10 workers evaluating patient data and moving it between disconnected systems.
    • A top insurance company operated a sizable team of case managers to process service requests, each of which took more than four hours to identify, classify, and assign to representatives.

    These highly varied companies had one thing in common: Their back-office workload was limiting productivity and, ultimately, growth. And the solution for each was the same: hyperautomation, anchored by a digital technology called Intelligent Document Processing (IDP). The Nividous hyperautomation platform leveraged its native IDP capabilities to help these companies reduce manual work by 80 to 90%, with other benefits ranging from error-reduction rates of 80% to turnaround-time improvements of 60% to 95%.

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    The value of automated data processing from very different business documents is clear across verticals. That explains why the IDP market is expected to grow at a compound annual growth rate of nearly 37% through 2026, reaching a global value of $3.7 billion that year. With such quick adoption, the question isn’t whether your company can benefit from this technology—it’s whether you can compete without it. This is an introduction to the new era of enterprise data processing, which, as anyone who struggles with back-office efficiency can tell you, begins with documents.

    Intelligent document processing is a technology that uses Optical Character Recognition (OCR) and Artificial Intelligence (AI) to convert unstructured or semi-structured data into structured formats for analysis and/or further automation. That’s the technical definition of IDP. In the rest of this comprehensive guide, we’ll explain what these terms mean, how IDP can help your business compete, and why automated document processing technology is a cornerstone of end-to-end digital process automation. But it might be helpful to start with a concrete example of IDP at work.

    IDP for Invoice Processing and Accounting Process Automation

    Intelligent automation can greatly improve accounts payable processes, and IDP plays an important role in this effort. Invoices come in many forms, from a standardized Quickbooks export to a few informal lines in an email. When you work with dozens or hundreds of vendors—and each one formats its invoices differently—it takes a lot of dedicated staff time just to move data from vendor forms into an ERP, accounting software, or both. Without AI, digital automation solutions can only work with structured data, and without AI, only humans can perform that structuring.

    Luckily, AI is here, and it’s a hallmark of IDP. In the above example, IDP bots identify, extract, and organize relevant data from all those invoices, freeing the human workforce for more creative tasks—as they do in Nividous’ accounts payable automation solutions. The resulting structured data can be imported into an ERP or accounting software. Better yet, deploy Robotic Process Automation (RPA) bots to automate the whole process—something we’ll discuss in more detail later in this article.

    The point is: IDP extracts relevant data from any record, regardless of layout—a capability that extends far beyond the accounting sphere. Because of its layout-agnostic ability to capture just the information you need from any type of document—from emails to images to PDFs—IDP saves billions of hours of work every year, with game-changing improvements to efficiency, accuracy, and capacity for innovation. But to begin to understand this form of intelligent data processing, you need to know the difference between structured, semi-structured, and unstructured data.

    Document Processing: Structured vs. Unstructured Data

    Every business document is an accumulation of data, whether it’s a client’s email message or an inventory spreadsheet. But that data isn’t necessarily organized into a form that software systems can interact with. In fact, most of the time, it isn’t.

    • Structured data is organized, usually into predefined fields like columns, rows, and cells. Excel spreadsheets are one example; a more back-end example would be a relational database like SQL, which generates new tables by mixing and matching datasets. This is how most business report generators operate, and it’s a central tool for data analytics.
    • Unstructured data isn’t programmatically organized. It’s information in myriad forms, from written reports to images to videos and more, including natural language, which is just the way humans write and speak for one another.
    • Semi-structured data splits the difference by appearing within documents that blend both structured and unstructured data. Emails are a common example; the body consists of natural language, which is unstructured by definition, while technical details like sender and recipient email addresses and timestamps are organized into tables, and, therefore, structured.

    Here’s the thing about operable business data: Roughly 80% of the time, this data is either unstructured or only semi-structured. Some stages of the digital automation process require structured data. Before an end-to-end automation system can proceed to performing a processing task, it may need to impose structure on semi-structured and unstructured data. And to do that without a vast workforce of human clerks, you need intelligent document solutions like IDP.

    How Intelligent Document Processing Works

    So how does a software system like IDP perform the very human task of scanning a document, recognizing what’s relevant and what isn’t, and extracting the valuable data while ignoring the rest? To perform intelligent data extraction, an IDP system uses two types of digital technology.

    The first is Optical Character Recognition, or OCR, a technology that recognizes letters, numbers, and other language-related characters in documents of all sorts, producing a text translation that software can manipulate. This technology has been around for decades, but without the addition of AI, it often falls short when confronting semi-structured and unstructured data.

    For example, think back to a traditional form for opening a bank account or filing your taxes. Rather than providing a simple line to fill out, these forms presented a row of boxes; even when spelling your own name, you’d have to place each letter in a separate box. That’s because banks and the IRS were digitizing these forms with a traditional OCR system, which required highly structured data—structured to the level of the character—to successfully translate typed or hand-written characters into a digitally usable format. To achieve true IDP, you need a second technology component.

    That second component is Artificial Intelligence (AI), which, for IDP, comes in at least three forms:

    • Machine Learning(ML) is software’s ability to continually improve accuracy through exposure to training data—with and without human intervention.
    • Computer Vision(CV) adds a form of machine understanding to the characters that OCR extracts from a document. So, rather than simply producing a text document, as in traditional OCR, computer vision can identify specific types of data, allowing it to organize that data in structured output. The CV software can recognize invoice numbers, for instance, regardless of where they appear in an unstructured document. That’s why we describe the Nividous’ IDP solution as using “OCR with CV” or “CV-based OCR.” It doesn’t just extract raw data; it recognizes and organizes that data while extracting it—and this CV employs ML to continuously improve accuracy.
    • Natural Language Processing (NLP) is the field of AI concerned with translating a human writer’s or speaker’s language into formats computers can process. With NLP layered over OCR and CV, Nividous IDP software can extract usable data from raw written documents like reports and emails.

    This combination of OCR and AI allows IDP to extract semi-structured and unstructured data from highly disparate sources, generating an output of structured data that’s ready for the next step—whether that’s advanced analytics or another stage of total process automation.

    Intelligent Document Automation and Broader Intelligent Automation

    Intelligent document processing is just one of the capabilities available through the Nividous platform, which provides all the components necessary for scalable business process automation. Rather than cobbling together an automation solution from multiple providers, which may require complex (and costly) integrations to function, the Nividous platform provides everything you need to achieve digital transformation.

    After IDP extracts the data necessary for virtually any business process—and exports that data in a fully structured form—RPA bots complete further rule-based tasks on any combination of legacy systems and custom applications, either on their own or in concert with human participants. A Business Process Management (BPM) system organizes work, orchestrating RPA- and human-driven tasks from start to finish. Meanwhile, ML with human feedback continually improves RPA behavior as the work scales—and it’s all available in one easy-to-use platform.

    3 Examples of Intelligent Document Processing

    The best way to understand the value of IDP is to explore how it functions in real-world applications. These three case studies show the breadth of this AI-driven automation solution.

    1. Automating Invoice Processing in the Manufacturing Industry

    A major manufacturer of custom mineral products faced an inefficient invoicing process with serious bottom-line effects. Not only were invoices delayed on intake, but human data errors were threatening vendor relationships. A team of more than four full-time employees had to manually copy data from hundreds of invoices and multiple vendors—each of whom formatted billing documents differently. Keeping up with the workload was a constant challenge.

    The solution was Nividous Smart Bots. In this case, these bots performed IDP data extraction from scanned and image-based documents. By extracting the unstructured and semi-structured data from all these invoices, organizing that data, and automatically importing it into an ERP, these Smart Bots saved more than 1,000 staff hours per month. They also completely eliminated manual data errors. Overall, the process turnaround time was nearly cut in half.

    Read the full case study.

    2. Automating Customer Onboarding in the Insurance Industry

    A top life insurance provider wished to expedite its customer onboarding process, including verification and fraud detection. That was a challenge; agents collected customer data on hand-held Android devices in the field, while the back office processed more than 500 of these records per day.

    The company partnered with Nividous to introduce IDP Smart Bots into a mobile app-based application form, pulling the necessary text and images from documents as varied as driver’s licenses, passports, and checks—with real-time data comparison for near-instant verification. Along with further RPA processes, this solution cut fraud rates by 50%, improved data accuracy by 90%, and reduced Full-Time Equivalent (FTE) process time by 60%.

    Read the full case study.

    3. Automating Exchange Bonus Claims in the Automotive Industry

    In another example of Nividous’ mobile-based IDP solutions, a large automotive company vastly improved its sales team’s process of claiming bonuses for successful car sales. Previously, back-office staff had to verify claims data from multiple documents, including driver’s licenses, invoices, registrations, and insurance forms. Then, they manually entered it into the local document management system.

    Nividous Smart Bots used their on-device IDP capabilities to pull data from these forms with a quick scan, automatically filling out forms on a mobile app. Sales representatives could verify all data and make corrections on the spot. The system then forwarded the ready-to-use forms to the company’s back-office systems for further automated processing via Nividous RPA bots. Overall, implementing Nidious automation reduced data errors by 80%, cut manual work by 90%, and improved turnaround time by 95%.

    Read the full case study.

    Intelligent Document Processing on the Nividous Platform

    In 2021, business research firm Everest Group named Nividous as a Major Contender in the global intelligent document processing market. This distinction is due to the Nividous platform’s “flexibility in processing different document types and formats and strong mobile extraction capability,” said Anil Vijayan, vice president of Everest Group.

    In addition to this on-device mobile data extraction, IDP from Nividous provides a range of out-of-the-box advantages. These include:

    • On-screen data extraction, which operates not unlike a human specialist to recognize correct information from visual presentations of digital documents.
    • An intuitive user interface that allows anyone to build custom IDP models with minimal IT involvement.
    • Native integration of AI and OCR leads to higher accuracy and quick results—without third-party software complications.
    • Total end-to-end process automation. With RPA and Business Process Management (BPM) systems built into the platform, users can automate complex processes—including human-bot task orchestration—all in one place.
    • Flexible licensing with a low total cost of ownership. Rather than charging per page like most IDP providers, the Nividous platform is available as a perpetual license or through yearly subscriptions, with no hidden fees.

    Every business runs on data, and that data lives in documents. Implement intelligent document processing to improve efficiency, accuracy, and data visibility—the key ingredient of growth-focused decision making—and to claim the competitive advantage in any industry.

    Contact us to get started today.

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