The financial industry is seeing a rise in the use of AI-based research, with robo-advisory solutions being one of its most prominent applications. By utilizing complex datasets and algorithms, these robo-advisory solutions can offer customized solutions that improve a person’s efficiency when it comes to investing, saving and retiring. The emergence of robo-advisors, algorithm-based digital products that evaluate customer data to automatically design and maintain investment portfolios, is a significant advancement in this industry. One of the drivers is the use of mobile applications in banking, insurance and other financial organizations, which has grown manifold due to the COVID-19 pandemic, and the trend is expected to witness further growth. Financial firms have opportunities to adopt AI-driven algorithm-based solutions, such as robo-advisory ones that can anticipate customer needs in advance and offer highly personalized services at the right time. The purpose of the research is to employ the extended unified theory of acceptance and use of technology (UTAUT) model to identify the factors that determine consumers’ decision to adopt robo-advisory in the financial services setting. The primary data includes 864 responses gathered from a pool of financial advisors. The study contributes to the literature by identifying the determinants of robo-advisory services by extending the UTAUT model.
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