Customer perception of AI-supported communication in insurance: a Rapid Review

Digitalization in the insurance industry has significantly accelerated in recent years, accompanied by a growing use of artificial intelligence in the automation of customer communication. Insurance are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support ptocess. The aim of this study is to synthesize the findings of existing peer-reviewed research on the impact of artificial intelligence technologies supporting customer communication on the customer experience in the insurance industry.Another objective is to explore the prerequisites for adopting an insurance solution, chatbot, and to present current trends in research and future research possibilities. The synthesis of findings is conducted using the Rapid Review methodology, following its established procedures and recommended tools. The relevant bibliographic overview was obtained through a review of studies indexed in the scientific databases WoS and Scopus, as well as sources of grey literature. The process of selecting relevant studies is mapped according to the PRISMA guidelines. The studies that passed the screening process based on predefined inclusion and exclusion criteria were subjected to detailed examination. For the purpose of knowledge synthesis, the following aspects were specified: theme of studies, data collection method, data analysis method, respondents and sample size, applied model, and examined factors. The study shows that current topics focus primarily on identifying positive and negative factors that influence customer feelings when communicating with a chatbot. The data obtained from the surveys were analyzed using the Partial Least Squares-Structural Equation Modeling method. The positive factors supporting the acceptance and intention to use chatbot technology include Trust, Perceived Usefulness, Perceived Ease of Use, Performance Expectancy, and Personalization. Among the negative factors identified were Privacy Concerns, Creepiness, Perceived Risk, and Effort Expectancy. The study has indicated several opportunities for further research.