Making smart decisions with RPA and Machine Learning

Machine learning, a subset of artificial intelligence adds intelligence to RPA bots. Machine learning models capture human knowledge as model parameters and then enable the RPA bots to take decisions like humans. It elevates RPA to “intelligent” RPA meaning it can address a larger variety of use cases. The multiple scenarios such as support from experts, handling of unstructured data, reviewing of responses, etc.

As an example, we added a machine learning model to enhance bot capabilities in the service provisioning and order entry space for a tier 1 service provider in the US. The existing process could not be automated entirely with RPA bots. Traditionally, an RPA bot would be paused till an expert is present to provide a decision where human judgement is needed. We created a machine learning model that could learn and eventually remove the need for human judgment-based decision making.

With help from the process expert team and machine learning experts, we collected decision metrics for the previous few months and analyzed them to build a text-based intelligent-decision-engine. As the response data was unstructured, we applied NLP and natural language understanding (NLU) techniques to data analysis and model building. We trained the prediction model with earlier responses dating a back few months, and then connected to the RPA bot. Now, when the RPA bot retrieves response data from the commissioning server, it calls the machine learning model via an API and proceeds in line with the model’s output. We removed the dependency on human judgment-driven decision-making, gaining an initial decision accuracy greater than 93%, something that will constantly improve with retraining.