Is Intelligent Document processing (IDP) same as Robotic Process Automation (RPA)? That is a popular question I frequently get from prospective clients. The short answer is that IDP is not RPA. IDP and RPA are complementary technologies with common objective to automate business processes. To better answer the question, it is important to take a brief look at them and compare.
What is Robotic Process Automation (RPA)?
Robotic Process Automation (or RPA, for short) is a well-known in IT industry to streamline enterprise operations and reduce cost. It can help businesses automate routine mundane rule-based tasks, enabling business users to focus on other higher-value tasks. The technology is highly effective when data is available in structured format with little to no variation. There are several RPA tools available in the market that help configure software, or a “Bot”, to capture information from these structured information sources to process a transaction, manipulate data, trigger responses, and communicate with other digital systems.
Over past several years, RPA technology has been successfully deployed across enterprises but is not perfect. It has its own share of challenges and limitations. Key notable ones are:
- Bots are predominantly rules driven and are extremely sensitive to any change in the structure of input content as well as the target. A simple change in interface can throw bots off-track and may put them back into development (configuration) and testing cycle.
- Enterprises have found that scaling and maintaining bots has been challenging, time consuming and costly endeavor.
Due to above characteristics, RPA is inherently unsuitable for extracting information from unstructured content with no fixed format, like documents.
What is Intelligent Document Processing (IDP)?
Intelligent Document processing attempts to address the challenge of extracting information from unstructured content sources i.e., documents (pdf and other formats), images, e-mails. It is estimated that up to 85% of data in an enterprise resides in unstructured sources and many back-office processes, like claims processing, collateral creation, etc. start with a document as a source. Hence, intelligent document processing is important for enterprises to get to the next level of automation and generate economic value.
Like RPA, IDP share the common goal of improving operational efficiency by automating mundane routine tasks thus enabling business users to focus on other higher-value tasks. However, unlike RPA, IDP does not employ any rules to achieve that objective. Intelligent Document Processing are driven by AI technology (OCR and NLP, specifically) to ingest documents, process them and extract information into desired structured format. The information extracted can then be inputted programmatically (via an API) into back-end corporate systems to achieve greater level of automation or can be stored in repository to augment decision making for strategic initiatives. Enterprises can realize additional benefits like information security, better customer service, higher employee satisfaction, higher level of accuracy and better regulatory compliance by leveraging IDP strategically in their business operations.
The real benefit of an AI driven IDP solutions is that it continuously learns from the data without any human intervention or configuration of rules in the system. In fact, IDP is quite the opposite of RPA in that regard – there are no explicit rules to be defined to extract information. The underlying machine learning algorithms deciphers rules by examining the patterns in the data and learn from it. The ability to learn automatically based on data makes the AI technology uniquely qualified for processing and extracting information from documents.
Like any other technology offering, there are several document processing solutions available in the market. They are mostly point solutions that are customized for short form documents like invoices, receipts, etc. with their own strengths and weaknesses. For a solution to be truly an intelligent document processing solution:
- It should be Domain Agnostic
- Flexible to adopt emerging technological advancements
- Support structured and unstructured data
- Provide ability to define and extract custom information (entities)
- Intuitive GUI interface for easy model training and validation
- Scalable to handle volumes of today’s enterprises
Introducing IDP Solution
IDP solution from args.ai is an AI driven Intelligent Document processing framework that is designed to extract information from unstructured content in various formats and complies with all the tenets of an IDP solution framework, as mentioned above. To briefly summarize some of the key features:
- Process short and long documents (100s of pages), alike. Large complex documents - like Credit Agreements, Real Estate Appraisals and Legal Contracts – can be easily processed
- Builds an intelligent outline for easy navigation, annotation, and validation of NLP models
- Extracts information (entities) and clauses from anywhere in the document – paragraphs and tables
- Dynamically maps entities in relation to each other as per the information ontology
- Scales horizontally to meet increasing information extraction requirements
The core of the solution is based on state-of-the-art NLP technology that leverages transfer learning techniques and makes the entire process of training machine learning models fast and easy via intuitive GUI interface. The framework can be deployed in any domain (industry) and requires only a modest amount of training data, even less for structure and semi-structured documents.
Conclusion
In this blog we briefly compared RPA with IDP and introduced InfoX as an intelligent document processing framework. RPA and IDP, both fall in the same domain of Information Extraction and strive to achieve the same automation goals. The difference is in the underlying approach– rule based for RPA vs AI driven for IDP - that separates the two and makes them suitable for different set of use cases for automation. Enterprises can achieve higher level of operational efficiency by strategically combining the two approaches thus improving customer service and employee satisfaction among other benefits.