Agentic AI as a tool for automating insurance sales: design and implementation

Abstract

Introduction. The article explores the potential of Agentic AI as a tool for automating the sales of insurance products. The study also analyzes the architecture of the agent system, including natural language processing (NLP) modules, client interaction modules, decision-making modules, and integration with CRM systems.

Materials and methods. The article examines decision-making algorithms that combine machine learning methods with rule-based approaches, enabling intelligent agents to autonomously initiate client interactions, provide personalized recommendations, and adapt to client behavior.The automation of insurance product sales is particularly important due to the growing digitalization of the industry, high competition, and the increasing demand for personalization. The scientific novelty of the work lies in a comprehensive approach that combines quantitative evaluation of agent systems in the insurance sector with implementation and design. The article outlines how autonomous intelligent agents can influence a company's economic indicators and enhance the client experience.

Results and discussion. The study also considers the impact on long-term forecasting accuracy, associated with client behavior, data volume, and the timelines of the pilot project implementation. The practical significance of the article is based on the development of an agent system and its implementation in a large insurance company.

Conclusion. The analysis demonstrated effective acceleration of application processing, reduction of acquisition costs, increased client retention, and significant improvements in ROI and conversion rates. The study focuses on the implementation of a pilot project for a single type of insurance product; further research could extend this approach to other products and market segments.