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.

Keywords: Environmental, Social, and Governance (ESG), Artificial Intelligence (AI), Financial Risk Assessment, Regulatory Compliance, Responsible AI

Complexity vs Empirical Score

  • Math Complexity: 1.0/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers
  • Why: The paper is a survey and industry analysis with no mathematical derivations or formulas, focusing on conceptual frameworks and business applications. Its empirical rigor is low as it relies on reported statistics and qualitative insights rather than backtested models or data implementation.
  flowchart TD
    A["Research Goal<br/>AI in ESG for Financial Institutions"] --> B{"Methodology"}
    B --> B1["Industrial Survey"]
    B --> B2["Data Collection"]
    
    C["Data/Inputs"] --> C1["Regulatory Requirements"]
    C --> C2["Stakeholder Data"]
    C --> C3["Financial Activity Data"]
    
    B1 & B2 --> D["Computational Processes<br/>AI Analysis"]
    D --> D1["ESG Pillar Applications"]
    D1 --> D2["Environmental AI Models"]
    D1 --> D3["Social AI Models"]
    D1 --> D4["Governance AI Models"]
    
    D --> D5["Key Considerations"]
    D5 --> D6["Data Quality & Privacy"]
    D5 --> D7["Model Robustness"]
    D5 --> D8["Responsible AI Ethics"]
    
    D --> E["Key Findings/Outcomes"]
    E --> E1["Transformative Potential"]
    E --> E2["Significant Challenges"]
    E --> E3["Recommendations"]
    E --> E4["Reference Architecture"]