IDP technology facilitates data extraction from various document formats and sources and organizes it in a structured way so that it can be used in end-to-end automation of document-centric processes. Intelligent Document Processing makes use of Artificial Intelligence (AI) and its sub=components, Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision (CV), along with Optical Character Recognition (OCR) to process the most difficult-to-automate data. With these technologies, the IDP solution captures, classifies, extracts, and validates data enabling improved data accuracy and end-to-end process automation.
- IDP is not just OCR. Traditional OCR can scan documents and transform them into a machine-readable structure but cannot understand the data. IDP contextually understands data by employing self-learning, and advanced AI techniques that allow more accurate, speedy data extraction.
- IDP is not Robotic Process Automation (RPA). RPA is an automation tool that runs on a defined set of rules to process specific tasks. RPA cannot make sense of the context on its own and requires human intervention or support from other technologies like IDP to produce automate processes that require decisions based on unstructured data.
Intelligent document processing, the next generation of automation, combines OCR and AI technologies to convert analog data (structured, unstructured, semi-structured) into a digital and usable format to integrate documents within business processes. Typically, the end-to-end IDP workflow consists of the following steps.
Document Classification is the process of classifying documents based on their format, structure, and type into predefined categories. Moreover, using AI-based technologies, multi-page documents can be automatically classified and separated by document type before it is further queued for data extraction.
Data Extraction involves rule-based OCR to digitalize the document and ML-based models to extract specific data. Nividous approaches data extraction using an ML-based model that sits on the top of OCR. You can use a library of pre-trained, domain-specific models, which are pre-populated with the correct fields for extraction.
The confidence score measures the efficiency of the models. The model grows stronger, delivering more accurate outcomes with the increasing number of processed documents and the amount of feedback provided. Context-based NLP is used to strengthen data accuracy.
Data Validation is a crucial step in detecting inaccuracies in extracted data. The extracted data goes through a series of validation rules for error detection and flagging inaccuracies for corrections.
Data Enrichment helps transform the collected data into more meaningful and contextual information. It is the appending or enhancing of collected data with relevant context obtained from additional sources. The domain-specific connectors and built-in intelligence help enrich data without requiring extra effort.
Verification involves human orchestration. It is a vital step for mainly two reasons: 1. The extracted or classified information may not always be accurate and require manual eyeballing. 2. Human interaction is sometimes necessary to handle exceptions, corrections, and approvals.
Integration is the final step in the workflow where the IDP Service Oriented Architecture helps the solution integrate seamlessly with the target system via RESTful APIs.
IDP is the gateway to unlocking the actual value of your data. According to IDC, worldwide data will exceed 175 zettabytes by 2025. The amount of enterprise data found in text, emails, PDFs, and scanned documents is growing exponentially. Imagine the volume of data locked in email alone – if this data is not serviceable for a given process, it poses a natural barrier to automation and digital transformation.
IDP transforms the raw data into structured information that can be further used in a business process, making end-to-end automation possible. Your data processing costs are reduced as AI-driven IDP continuously learns from human feedback; when combined with RPA, you can significantly improve straight-through-processing (STP). IDP solutions are easy to set up and start with, especially with pre-built domain-specific solutions, and often 5-10x faster.
- Faster Data Processing
- Enhanced Accuracy
- Reduced Processing Cost
- Improved Process Efficiency
- Ease of Use
- Higher Straight Through Processing
- Self-Improving Processing
- Enterprice-Grade Automation
IDP is an essential component of intelligent automation – which is sometimes called cognitive automation, hyperautomation, or digital process automation. When combined with RPA, business process management systems, and other automation tools, IDP makes it possible for businesses to achieve true intelligent automation – a roadmap to embracing a more holistic approach toward automation.
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 conjunction 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.
The intelligent Document Processing solution extracts and organizes data across business functions and for different industries right out of the box. The best way to understand the importance of IDP is by exploring its application in real-world business cases.
Digitizing Paper Documents Scan paper-based documents and convert them into machine-readable file formats with the use of IDP.
Capturing Right Information Intelligent data extraction pulls the right data you want from various document formats for further processing, storage, and use within the end-to-end business process.
Avoiding Manual Data Entry Eliminate manual data entry into your EHR, ERP, mortgage application, and loan origination system (LOS) by combining IDP solutions with RPA and BPM systems.
Decrease Manual Errors Significantly decrease manual errors and delays in document processing and improve data accuracy.
Meet Regulatory Compliance Meet your ongoing regulatory compliance requirements and achieve improved transparency in storing and organizing documents.
1. Automating Accounts Payable: IDP, an integral part of intelligent automation, plays an essential role in automating accounts payable processes. Manual processing of hundreds of invoices in varied formats takes up significant staff hours. Moreover, each vendor formats invoices differently, making it a tedious, error-prone manual task. IDP bots identify, extract, and organize relevant data from all types and forms of invoices, freeing the human workforce for more valuable tasks. The resulting structured data can be imported into an ERP or accounting software. Better yet, deploy RPA bots to automate the whole process end-to-end.
Success Story: Nividous Bots with native AI capabilities were deployed to perform IDP data extraction from scanned and image-based documents for a leading manufacturer of custom mineral products. The customer saved over 1000 staff hours per month and eliminated manual data errors with the help of Nividous Bots. The Bots extracted the unstructured and semi-structured data from all invoices, organized it, and automatically imported it into an ERP. Overall, the automation nearly cut the process turnaround time in half.
2. Automating Supply Chain Operations: Supply chain professionals get bogged down by a range of manual tasks, including but not limited to procurement, order processing, invoicing, warehousing, and manufacturing, requiring movement of data across multiple discrete systems. IDP solutions help extract automatically, transfer and validate data using AI/ML technologies, connecting disparate data sources and computer systems without costly custom integrations.
Success Story: A leading logistics company used Nividous Smart Bots to automate its documentation processes, including the letter of credit amendment process and validation of bill of lading, load report, insurance, certificate of origin, etc. The automation enabled an 80% reduction in human dependency, 60% reduction in TAT, and >900 savings of monthly man-hours.
3. Automate Customer Onboarding: Manual verification of several identity documents makes the customer onboarding process highly daunting, especially when it comes to Know-Your-Customer (KYC). It is an integral part of the onboarding process and involves significant operational effort for such document validations. Put IDP solution to use to automatically extract relevant information and validate it against the application form to ultimately expedite the process TAT and reduce staff hours and manual errors.
Success Story: For a leading insurance provider, its manual customer onboarding process involved field agents collecting data through hand-held android devices. Back-office staff processed more than 500 of those records daily. The company deployed Nividous’ IDP Smart Bots that not only pull necessary information, including passports and driver’s licenses but verify data in real-time. Along with RPA Bots involving the automation of different processes, the company cut fraud rates by 50% and improved data accuracy by 90%.
Start automating your end-to-end business processes using Nividous’ integrated Intelligent Automation (IA) platform. It consists of RPA, IDP, BPM, and analytics capabilities. The platform leverages its proprietary OCR and computer vision-based ML models and NLP capabilities for the classification, extraction, and validation of data from different types of documents, applications, and databases. It provides an easy-to-use low-/no-code interface that allows business users to create new document extraction models without the need to rely on data scientists.
Key IDP Capabilities of the Nividous Platform
- Native AI, RPA, and BPM/Process Orchestration
- Strong NLP capabilities to make sense of unstructured data
- Native mobile application supporting on-device data extraction
- Full-featured low-code BPMN 2.0 compliant workflow engine
- Business user-facing GUI with simple drag-and-drop features
- An intuitive user interface for human-bot work orchestration
- Out-of-the-box packaged solutions for various domains
- Slice and dice reporting and analytics dashboards with GUI for end-users
- Flexible licensing with a low total cost of ownership
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.