Environmental, Social, and Governance (ESG)

AI in ESG for Financial Institutions: An Industrial Survey ArXiv ID: 2403.05541 “View on arXiv” Authors: Unknown Abstract The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey’s insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI’s strengths while mitigating its risks within the ESG domain. ...

ESG driven pairs algorithm for sustainable trading: Analysis from the Indian market ArXiv ID: 2401.14761 “View on arXiv” Authors: Unknown Abstract This paper proposes an algorithmic trading framework integrating Environmental, Social, and Governance (ESG) ratings with a pairs trading strategy. It addresses the demand for socially responsible investment solutions by developing a unique algorithm blending ESG data with methods for identifying co-integrated stocks. This allows selecting profitable pairs adhering to ESG principles. Further, it incorporates technical indicators for optimal trade execution within this sustainability framework. Extensive back-testing provides evidence of the model’s effectiveness, consistently generating positive returns exceeding conventional pairs trading strategies, while upholding ESG principles. This paves the way for a transformative approach to algorithmic trading, offering insights for investors, policymakers, and academics. ...

Corporate Social Responsibility and SustainableFinance: A Review of the Literature ArXiv ID: ssrn-3698631 “View on arXiv” Authors: Unknown Abstract Corporate Social Responsibility (CSR) refers to the incorporation of Environmental, Social, and Governance (ESG) considerations into corporate management, finan Keywords: Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG), Sustainable Investing, Corporate Management, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on theoretical definitions and conceptual frameworks of CSR/ESG, with no mathematical formulas or advanced derivations. Empirical rigor is low as it synthesizes existing studies rather than presenting new backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Review literature on CSR & sustainable finance"] --> B["Data: 100+ peer-reviewed studies (2010-2024)"] B --> C["Method: Systematic literature review & thematic analysis"] C --> D["Computation: Thematic coding & trend analysis"] D --> E["Key Findings:"] E --> E1["ESG integration improves long-term returns"] E --> E2["Regulatory pressure drives adoption"] E --> E3["Social factors remain under-researched"]