Home The Synergy of IDP and RPA

The Synergy of IDP and RPA

by sol-admin
166 views

Intelligent Document Processing, or IDP, is increasingly seen as a critical next step to increasing process automation and handling the huge amount of inflowing data. But what is IDP exactly and how is it better to combine IDP with RPA? Get insights from Alexander Goerke, CEO and Founder of Skilja, a company developing essential technologies for understanding documents:

Intelligent Document Processing is an extension of classical data capture technology. It aims to take documents that were created for human readers and make them intelligible to machines. Typically, data capture has been used in the past to automate predefined document-driven business processes using a variety of different technologies including OCR, Forms Recognition, or Anchor-based detection of keys in a document.

Data capture is applied to fixed forms and semi-structured documents such as invoices and order confirmations to automate repetitive cognitive tasks. With the help of rules or machine learning, the patterns that allow humans to recognize entities in documents are identified so that the IDP software can repeat a task over and over again. We hesitate to call this artificial intelligence (AI) and prefer the term “cognitive technologies“ as the software actually tries to mimic human understanding and learn from human input.

IDP is the next generation of data capture and is typically applied to all kinds of documents, even totally unstructured contracts and correspondence, learning from human input how to understand the content. In contrast to enterprise data capture in the past, IDP typically does not require a dedicated setup and instead “learns” from the “human in the loop“ and gets better over time. The technologies that enable IDP include a variety of syntactic and semantic analysis steps combined with statistical evaluation and deep learning neural networks that are used in a complex background validation and learning service to continuously create and enhance the knowledge needed for document understanding.

Classification is of course a vital part of this.

RPA on the other hand is a consumer of the results. RPA is performing tasks that can be codified (scripted) – often interfacing between two systems – and therefore rules-based. It sends documents for interpretation to an IDP server and then can use the structured output of IDP to make automated rules-based decisions or simply transfer the data into another system.

IDP is a vital part of scaling and speeding up processes that require human interaction to understand data in documents. Intelligent automation is the combination of IDP with RPA because it bridges the gap between unstructured data, human verification/validation activities, and the structured data that is needed to automate processes. Not only are processes accelerated, but they can also run round the clock and be scaled almost arbitrarily (in the cloud almost without additional cost). With IDP and RPA, even small tedious tasks on a desktop can now be automated. Even if only a few dozen surveys or timesheets need to be entered into a backend every day, that will still take up an hour each day of somebody’s time. Nowadays with IDP, it is easy to set up a system that will take this task over from an office worker and allow them to focus on other tasks.


The Source