Enhancing Business Process Automation Through NLP-Based Email Interpretation in Pega Systems

Authors: Sairohith Thummarakoti

Country: USA

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Abstract: Email remains the primary customer interaction channel in today’s business environment but is generally followed by manual processing procedures regarding sorting and replies, leading to significant inefficiencies and risks. This paper presents an all-encompassing solution for automating email reading and routing through Pega’s Natural Language Processing, highlighting its real-world uses in the areas of the healthcare and financial services industries. Pega’s NLP engine effectively categorizes incoming emails, identifies relevant information, and routes them to the relevant workflows within the organization using machine learning-based text classification, sentiment analysis, and named-entity recognition. We suggest an overall end-to-end architecture comprising the Pega Email Listener, Text Analyzer, Decision Tables, and Case Management to handle large unstructured communications.
A sophisticated architecture is outlined, which includes data preparation, model training in an adaptive fashion using PegaPrediction Studio, and testing using metrics including accuracy and F1-score. The architecture supports incremental learning through user guidance and incremental retraining, allowing it to adapt to changing language trends and business rule patterns. Examples include but are not limited to appointment scheduling, patient query triaging, insurance claims processing, and loan application intake, where we demonstrate how Pega's Natural Language Processing (NLP) supports intelligent automation to reduce manual labor and response time and enhance compliance. Experimental results, supported by visual performance metrics, verify the effectiveness of the models, highlighting the entity extraction accuracy and better sentiment detection compared to baseline practices. The future trends explored include multimodal AI, federated learning for enabling privacy-preserving training in the health sector, and proactive interaction through predictive natural language processing (NLP). This study confirms that incorporating enterprise NLP into case routing rejuvenates workflow orchestrations and provides a foundation for scalable, safe, and customer-centered digital operations.

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Paper Id: 232477

Published On: 2023-05-04

Published In: Volume 11, Issue 3, May-June 2023

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