For instance, in the claims processing world, nearly every aspect of the process remains paper-based. People mail or email physical or scanned documents to a system, where humans must then review and classify them by hand.
Inability to process unstructured data is the Achilles Heel for many unsuccessful RPA implementations. Organisations that want to automate complex data-driven business processes need to turn to advanced artificial intelligence (AI) technologies to enhance the effectiveness of their RPA investments. Integrating Natural Language Processing (NLP) and Machine Learning (ML) into RPA solutions can help with analysing, understanding and classifying unstructured data – unlocking the ability to automate a substantial amount of business processes.
Here are four tips for overcoming the challenges of processing unstructured data to make automation dreams a reality:
·Integrate cognitive document automation (including foundational AI) with the RPA solution to automatically process documents needed for a business process and achieve the most seamless workflows.
·Prioritise flexibility. Document submission should be “smart” enough to allow customers to switch back and forth between channels during the same process.
·Make sure your solution can scale to very large document volumes and distributed work environments.
·Look for global applicability, with support for multiple user interface and OCR languages.