Exploring Java for AI-Powered Chatbots in the Insurance Industry

Abstract

The insurance industry is undergoing a digital transformation, and artificial intelligence (AI)-powered chatbots are at the forefront of this revolution. AI chatbots are enhancing operational efficiency, improving customer experience, and reducing costs by automating tasks such as claims processing, fraud detection, and policyholder assistance. While Python is often the language of choice for AI development, Java is gaining popularity for developing AI-driven chatbots due to its scalability, performance, and security features. This paper explores the role of Java in the development of AI chatbots, focusing particularly on its use in the insurance sector. By leveraging Java-based machine learning tools like Deeplearning4j and Apache OpenNLP, these chatbots are equipped to analyze and interpret vast amounts of data for decision-making. Logistic regression, a statistical technique for binary classification, is highlighted as a key machine learning model used for fraud detection in insurance claims. The paper discusses how these Java frameworks and algorithms contribute to the development of efficient and intelligent chatbot systems that support insurance operations, improve customer satisfaction, and ensure data security.