金融机构 ESG 中的人工智能:行业调查

Jun Xu
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引用次数: 0

摘要

人工智能(AI)与环境、社会和治理(ESG)计划在金融领域的蓬勃融合,代表着一种范式的转变,使金融实践更易实现且更加公平。本文通过对行业前景的调查,阐述了人工智能在加强环境、社会和治理框架方面的必要性和影响。随着严格的监管要求和利益相关者意识的提高,金融机构(FIs)越来越不得不采用环境、社会和公司治理标准。我们的调查对人工智能在环境、社会和公司治理三大支柱领域的应用进行了分类,说明了人工智能如何增强分析能力、风险评估、客户参与、报告准确性等。此外,我们还深入探讨了与数据使用和模型开发相关的重要考虑因素,强调了数据质量、隐私和模型稳健性的重要性。本文还探讨了负责任和可持续人工智能的必要性,强调了在与环境、社会和公司治理相关的银行业务流程中部署人工智能的道德层面。总之,我们的研究结果表明,虽然人工智能为银行业的环境、社会和治理提供了变革潜力,但它也带来了重大挑战,有必要认真加以考虑。本文的最后一部分综合了调查的见解,提出了在 ESG 实践中采用人工智能的前瞻性观点。最后,我们为未来的研究和发展提出了参考架构建议,主张采用一种平衡的方法,既能利用人工智能的优势,又能降低其在 ESG 领域的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI in ESG for Financial Institutions: An Industrial Survey
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.
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