Design and deployment of NLP-driven conversational agents in Java for the Insurance Sector

This paper presents the design, development, and deployment of a Natural Language Processing (NLP)-driven conversational agent built in Java, specifically customized for the insurance sector. The growing demand for 24/7 customer support, coupled with the need to reduce operational costs and improve service delivery, has made intelligent chatbots a compelling solution in the industry. Leveraging open-source Java NLP libraries such as Apache OpenNLP and Stanford CoreNLP, the proposed agent performs intent recognition, entity extraction, and dynamic dialogue management to assist users with tasks including policy inquiries, claim filing, fraud alerts, and live agent handoffs. The system is designed using a modular, scalable architecture that integrates with existing insurance infrastructure and adheres to strict data privacy regulations such as the NDPR and GDPR. Performance evaluation showed over 93% intent accuracy and high user satisfaction, with notable improvements in customer interaction speed and task completion. The study also discusses implementation challenges and outlines future enhancements, such as multilingual support, voice integration, and transformer-based model adoption. This research offers a replicable framework for deploying secure, efficient, and adaptable conversational agents within highly regulated industries.