Sustainable FinTech: Machine Learning Approaches for Green Investment Portfolios

Abstract

The blistering emergence of the Financial Technology (FinTech) provided the opportunity to alter the traditional priorities of investments to use the information-based decisions made in accordance with the advanced machine learning (ML) algorithms. On the same note, the world financial industry is under pressure, and it is underwriting pressure to be sustainable and environmentally friendly in their investments. This article presents a framework of sustainable finance using machine learning, which is related to the development of green portfolios, the assessment of carbon risks, and the ethics-driven optimization of finance. It is suggested that the supervised learning to classify assets, the reinforcement learning to optimally rebalance the portfolio, and the explainable artificial intelligence (XAI) to make decisions clear can be incorporated into one model. The empirical research of ESG (Environmental, Social, and Governance) data of MSCI and Refinitiv data use reveals that there is an increment in ratings of portfolio sustainability by 15 percent, and a growth in risk-adjusted returns by 11 percent over a base model. This is because the findings can be used to establish the extent to which ML-enabled FinTech applications can serve to support the realization of sustainable investment goals throughout the world and align financial growth with the green strategy.