Business process which involves documents need robust Intelligent Document Processing (IDP). The documents could be structured, semi-structured, or unstructured. The common goal is efficient classification and extraction of information. The advancement in computer vision, Artificial Intelligence, Machine Learning, and NLP techniques has provided the much-required lease of life to the legacy OCR / ICR solutions that were languishing with sub-optimal results and made human in loop mandatory.
Robotic Process Automation (RPA) is a mainstream affair in the digital world. A lot of mundane tasks are now done by machines (Bots). With more stress on automation and achieving operational excellence, it is highly necessary to have the Bots do the heavy lifting of monotonous tasks while the humans can take care of ones that involve decision making. For an RPA bot to be able to perform its task it needs to have reliable data required and SOPs defined. With advancements in technology Bots can be intelligent and perform more complex tasks that currently require human intervention. So the only thing left to maximize your RPA investments is to ensure that the BOT is fed clean data. That is where IDPs come into the picture.
RPA and IDP- Teamwork creates magic
The RPA and IDP go hand in hand for all document-driven process automation scenarios regardless of the domain it has been put to use may it be Mortgage, Insurance, Health care, or Medical Records analysis. The overall success of automation heavily depends on the thorough understanding of the documents which the underlying IDP should provide for RPA to be successful. In simplistic terms, there are three major stages in any document processing requirement,
- Document Classification
- Data extraction from documents
- Data validation and exception handling
The underlying IDP solution which fuels the RPA engine must be efficient and robust. The IDP solution should understand the data it is processing which is the key differentiator between the traditional OCR-based solutions vs the IDP. IDP solutions should have an insatiable hunger to learn and understand more document types and data inside those docs. All this with very little human intervention during the training process. This means that an IDP that is generic could fall short of what is required. Getting a finely tuned IDP that is built grounds up for a specific function will yield the best results faster.
The section below covers two use cases that the RPA and IDP duo can effectively handle and bring a boost in productivity, efficiency, and operational excellence.
Most of the solutions around the mortgage life cycle may it be Loan Setup or performing an Audit or preparation of secondary market packages or custom QC are the potential candidates of RPA. However easier said than done, as the Mortgage documents lack structured data the level of automation achieved today is way below the potential it has. To achieve the potential automation goal, the RPA needs a robust, accurate, and efficient IDP.
The mortgage documents due to their inherent complexity necessitate the need for human involvement in the analysis and segregation of information. To achieve maximum automation this becomes a deterrent. The solution to this is an IDP platform that can handle these document variations and provide accurate inputs.
You can see how a IDP that is specifically designed for Mortgage can help you process mortgage documents better and enable your RPA bots to work more efficiently here – Mortgage Originations – DocVu AI
With more and more companies embracing digital invoicing, there are still pockets in which the invoices are submitted as individual files. In addition, there is no standard format for invoices, each vendor has his own format. With the requisite data though being present in all these invoices, it is tough for the Bots to cater to a seamless automation process. Again, the key here is a modern IDP that is intelligent enough to classify and extract crucial information irrespective of various formats.