LEVERAGING BIG DATA ANALYTICS FOR SUSTAINABLE INVESTMENT DECISIONS AND GREENWASHING DETECTION

Abstract

This study looks at how big data analytics affects the uptake and clout of sustainable investing approaches. Big data analytics has become a vital tool for investors looking to incorporate sustainability considerations into their portfolios, as the significance of environmental, social, and governance (ESG) issues in investment decision-making continues to expand. The main conclusions show that big data analytics helps investors make better investment decisions by providing them with deeper insights into ESG-related risks and opportunities. Investors can identify emerging trends, evaluate the possible impact of ESG factors on financial performance, and assess company performance on sustainability metrics by analyzing large volumes of structured and unstructured data from various sources, including social media, news articles, financial reports, and satellite imagery. Furthermore, the utilization of big data analytics enables the creation of creative investment plans that complement sustainability objectives. Investment opportunities that yield competitive financial returns and positive social and environmental benefits can be identified by investors through the use of advanced analytics techniques such as machine learning and predictive modeling. The paper additionally underscores the obstacles and constraints linked to the application of big data analytics in sustainable investing, such as concerns over data quality, privacy, and the requirement for specialist knowledge in data analysis. Investors, data providers, regulators, and other stakeholders must work together to address these issues and guarantee the transparency and integrity of sustainability data and analytical techniques.