Integration of Robo-Advisors and Environmental,Social and Governance Investing

With fast development and widespread use of Fin-tech currently, as one important part of it, Robo-advisors, which feature in driving asset allocation and portfolio management by algorithm, are applied in practice. At the same time, the symbol of the concept of responsible investment, Environmental,Social and Governance (ESG) investing, has become a mature investing field from a newborn concept. An integration of an investing tool and an investing field has great potential. This paper aims to construct a theoretical integration framework of robo-advisors and ESG investing from four dimensions: data integration, investor preference identification, algorithmic mechanism design, and compliance and transparency, by reviewing existing literature. This integrated model not only enhances the accessibility, personalization, and sustainability of investments, but also faces challenges such as insufficient data standardization, limited algorithm interpretability, and weak market adaptability. In the last, this paper gives a conclusion that the future focus should be on the promotion of the establishment of unified ESG data standards, finding optimization algorithms of intelligent methods for investor preference identification, and the development of explainable artificial intelligence (XAI) to foster the development of transparent, compliant, and efficient ESG-oriented robo-advisory systems.