Authors: Prof. Abhijit Chakraborty, Vinayak Ramakrishna Bhat, Samartha Vinayak Hegde, Prasanna Vighneshwara Hegde
Abstract: The rapid expansion of sustainable investment has created an urgent demand for reliable, comprehensive, and verifiable ESG information. Yet, despite its growing importance, the field continues to struggle with fragmented reporting standards, selective disclosures, and the increasing prevalence of greenwashing, all of which undermine the integrity of sustainability-focused financial decisions. This study provides an in-depth qualitative exploration of how Artificial Intelligence (AI) and Big Data are beginning to address these persistent issues and reshape the foundations of sustainable investment strategies. Drawing on a wide range of scholarly publications, institutional reports, and contemporary discussions from 2020 to 2025, the research examines how emerging digital tools are being used to interpret and validate sustainability performance. The literature shows that AI techniques such as natural language processing, intelligent screening algorithms, and pattern recognition models are improving the credibility of ESG evaluations by identifying inconsistencies, revealing undisclosed risks. Big Data strengthens this process by incorporating diverse and externally sourced evidence, including satellite-based environmental monitoring, supply-chain traceability systems, climate-risk datasets, and real-time operational. These combined capabilities not only enhance transparency but also reduce information asymmetry, allowing investors to form a more accurate understanding of a firm’s sustainability behavior and long-term risk exposure. The study finds that the use of AI and Big Data is gradually shifting sustainable investing from a disclosure-driven model dependent on voluntary corporate reporting to a more evidence-based and analytically rigorous approach. This transition supports stronger investor confidence, encourages more accountable corporate behavior, and aligns investment decisions more closely with global sustainability objectives. While challenges remain, including data standardization and ethical concerns in AI use, the overall trajectory suggests that AI and Big Data are poised to become essential pillars of future sustainable finance. This study examines how advanced analytical technologies contribute to improving the reliability, transparency, and investment relevance of ESG assessments. Using secondary data from a sample of forty publicly listed companies across multiple industry sectors, the study employs descriptive statistics, sector-wise analysis, regression modelling, and one-way ANOVA to examine patterns in ESG performance and sustainability risk. The analysis reveals significant sectoral differences in ESG scores, with technology and healthcare firms demonstrating relatively higher sustainability performance compared to energy and manufacturing sectors. Regression results indicate a strong negative relationship between ESG scores and sustainability risk, suggesting that higher sustainability performance is associated with improved risk management outcomes. The findings also show that firms adopting AI-driven analytics and structured sustainability reporting practices tend to achieve higher ESG scores and lower risk exposure.
