Literature and studies on AI and sustainable finance
Navigating the volume of AI-related publications, and selecting the ones with actual relevant insights on AI applications for sustainable finance, can quickly become overwhelming.
We are listing here our own curated selection of publications, with a brief summary of the key messages for each document. This does not pretend to be comprehensive, but it shall help you spare time in identifiying relevant literature and studies. (Last updated 28. August 2026)
Selected governance frameworks
OECD - Recommendation of the Council on Artificial Intelligence
Alongside benefits, AI also raises challenges for our societies and economies, notably regarding economic shifts and inequalities, competition, transitions in the labour market, and implications for democracy and human rights. With its first version issued in 2019, the Recommendation on Artificial Intelligence was the first intergovernemental framework on AI and is still the reference on AI governance. It aims to foster innovation and trust in AI by promoting the responsible stewardship of trustworthy AI while ensuring respect for human rights and democratic values. There Recommendations were updated in 2023 and gain in 2024 to reflect technological and policy developments, including generative AI .
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NIST - AI Risk Management Framework
The NIST (US National Institute of Standards and Technology "Artificial Intelligence Risk Management Framework" (RMF) is one of the most widely recognized standard globally when it comes to designing risk management approaches to AI deployment. The goal of the RMF is to offer a tool to the organizations designing, developing, deploying, or using AI systems to help promote trustworthy and responsible development and use of AI systems. The RMF is intended to be voluntary, rights-preserving, non-sector-specific, and use-case agnostic. In 2026, the framework is currently under review.
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NIST - AI Risk Management Framework: GenAI Profile
This document is a cross-sectoral "profile" of and companion resource for the NIST "AI Risk Management Framework" (RMF) with a focus on Generative AI specifically. A "profile" is an implementation of the AI RMF functions, categories, and subcategories for a specific setting, application, or technology – in this case, Generative AI – based on the requirements, risk tolerance, and resources of the Framework.
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ISO/IEC 42001:2023 – Artificial Intelligence Management System
ISO/IEC 42001 is the international standard for an AI Management System (AIMS) for establishing, implementing, maintaining and continually improving governance of AI within organisations. It provides a structured approach to managing AI-related risks and opportunities, including policies, responsibilities, controls, monitoring and continual improvement.
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EU - Assessment List for Trustworthy AI
The Assessment List for Trustworthy AI (ALTAI) was developed in 2020 by an expert group of the EU AI Alliance. It is intended to help organisations identify how proposed AI systems might generate risks, and to identify whether and what kind of active measures may need to be taken to avoid and minimise those risks. Organisations can use it flexibly and draw on elements relevant to the particular AI system from the list, or add elements to it as they see fit.
moreMarket Intelligence
The AI Index Report 2026
Issued by Stanford University, the "AI Index Report" is the most authoritative source globally to provide a state of the progress and adoption of AI across various domains of our societies. As the authors describe themselves: "The AI Index continues to lead in tracking and interpreting the most critical trends shaping the field—from the shifting geopolitical landscape and the rapid evolution of underlying technologies, to AI’s expanding role in business, policymaking, and public life." The report is a goldmine of information.
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Generative Artificial Intelligence in Finance
The rapid acceleration in the pace of AI innovation in recent years and the advent of content generating capabilities have increased interest in AI innovation in finance, in part due to the user-friendliness and intuitive interface of GenAI tools. Currently, the use of GenAI in financial markets involving full end-to-end automation without any human intervention remains at early phase, but its wider deployment could amplify risks and give rise to new challenges. This OECD paper presents recent evolutions in GenAI and its slow-paced deployment in finance, analyses the potential risks from a wider use of GenAI tools by financial market participants, and discusses associated policy implications.
Swiss Banking Association, GenerativeAI in Banking - a comprehensive overview, 2025, https://www.swissbanking.ch/fr/themes/numerisation-innovation-et-cybersecurite/intelligence-artificielle-et-donnees
Lim, T., Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways, 2024, https://doi.org/10.1007/s10462-024-10708-3
Xu, J., AI in ESG for Financial Institutions: An Industrial Survey, 2024, https://arxiv.org/abs/2403.05541
Brière, M., Artificial Intelligence for Sustainable Finance: Why it May Help, 2024, https://ssrn.com/abstract=4252329
Elouidani, R., et al., Artificial Intelligence for a Sustainable Finance: A Bibliometric Analysis, 2023, https://doi.org/10.1007/978-3-031-26384-2_46
Al-Sartawi, A., et al., The role of artificial intelligence in sustainable finance, 2022, https://doi.org/10.1080/20430795.2022.2057405
Boukherouaa, E., et al., Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance (DP/2021/024), 2021, https://www.imf.org/en/Publications/Departmental-Papers-Policy-Papers/Issues/2021/10/21/Powering-the-Digital-Economy-Opportunities-and-Risks-of-Artificial-Intelligence-in-Finance-494717
KPMG Switzerland, The Use of AI in Sustainable Finance, 2024, https://kpmg.com/ch/en/insights/esg-sustainability/sustainable-finance/artificial-intelligence-use.html
IMD, Leveraging AI for Smarter Sustainability, 2024, https://www.imd.org/
Behera, I., et al., The Societal Impact of Artificial Intelligence in Sustainable Investment Strategies, 2024, https://www.igi-global.com/chapter/the-societal-impact-of-artificial-intelligence-in-sustainable-investment-strategies/345123
Adeoye, O., et al., Artificial Intelligence in ESG investing: Enhancing portfolio management and performance, 2024, https://ijsra.net/sites/default/files/IJSRA-2024-0305.pdf
Pashang, S., et al., AI for Sustainable Finance: Governance Mechanisms for Institutional and Societal Approaches, 2023, https://doi.org/10.1007/978-3-031-21147-8_12
Boston Consulting Group, AI and the Next Wave of Transformation, 2023, https://www.bcg.com/publications/2023/ai-and-the-next-wave-of-transformation
Network for Greening the Financial System, NGFS Scenarios Portal, 2024, https://www.ngfs.net/ngfs-scenarios-portal/
Our World in Data, Artificial Intelligence, https://ourworldindata.org/artificial-intelligence
Foundational Papers
Hinton G. et al., Learning representations by back-propagating errors, 1986, National Institute of Standards and Technology (NIST), https://doi.org/10.1038/323533a0
Vaswani, A., et al., Attention Is All You Need, 2017, https://arxiv.org/abs/1706.03762
Research Papers
Greenblatt, R., et al., Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI/ Hugging Face hacking incident, 2026, https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/
Kutasov, J., et al., Teaching Claude Why, 2026, https://alignment.anthropic.com/2026/teaching-claude-why/
Betley, J., et al., Training large language models on narrow tasks can lead to broad misalignment, 2026, https://www.nature.com/articles/s41586-025-09937-5
Panfilov, A., et al., Stealing Reasoning Traces from Proprietary LLM APIs, 2026, https://arxiv.org/abs/2608.09867
Mousavian Anaraki, S., et al., Large language models for sustainability reporting: A systematic review and research agenda, 2025, https://doi.org/10.1016/j.sftr.2025.101494
Calamai, T., et al., Corporate Greenwashing Detection in Text - a Survey, 2025, https://arxiv.org/abs/2502.07541
Zeng, F., et al., An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior, 2025, https://doi.org/10.1371/journal.pone.0316287
Li, Y., et al., Machine learning detection of manipulative environmental disclosures, 2025, https://www.nature.com/articles/s41598-025-29621-y
Lynch et al., Agentic Misalignment: How LLMs could be insider threats, 2025, https://www.anthropic.com/research/agentic-misalignment
Hauser, L., et al., Corporate Biodiversity and Water Impact and Risk: Seven Key Principles for Leveraging Insights From Satellite Remote Sensing, 2025, https://doi.org/10.1029/2024EF005474
Leippold, M., et al., Automated Fact-Checking of Climate Change Claims with Large Language Models, 2024, https://arxiv.org/abs/2401.12566
Colesanti-Senni, C., et al., Using AI to Assess the Decision-Usefulness of Corporates’ Nature-related Disclosures, 2024, https://ssrn.com/abstract=4860331
Kotsch, R., Network Analysis of the Global Trade of "Hot Air": Key Lessons for the Paris Agreement, 2024, https://ssrn.com/abstract=5038486
Schwendner, P., et al., Bio-Value-at-Risk: A Concept to Assessing the Implications of Biodiversity Risks on Portfolio Management using Geospatial Analysis, 2024, https://ssrn.com/abstract=4784271
Ni, J., et al., CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools, 2023, https://ssrn.com/abstract=4476733
Caldecott, B., et al., Spatial finance: practical and theoretical contributions to financial analysis, 2022, https://doi.org/10.1080/20430795.2022.2153007
Patterson, D., et al., The Biodiversity Data Puzzle, 2022, https://www.wwf.org.uk/sites/default/files/2022-12/The-Biodiversity-Data-Puzzle.pdf
Betz, R., et al., The Carbon Market Challenge: Preventing Abuse Through Effective Governance, 2022, https://www.cambridge.org/core/books/carbon-market-challenge/9261122253200C956EAF02B5C9AF53C8
Leippold, M., et al., ClimateBERT: A Pretrained Language Model for Climate-Related Text, 2021, https://arxiv.org/abs/2110.12010
Responsible AI Guidance
Responsible Use of Generative AI - Playbook for business leaders
The purpose of this playbook (2025) by UC Berkeley is to help product managers and organizational decision makers understand the GenAI risks and ensure genAI is rolled out and used "responsibly" in their organization. Pages 16 to 21 are the most useful ones, providing a short but clear summary of the key risks linked to GenAI.
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Responsible AI Playbook for Investors
The "Responsible AI Playbook for Investors," published by the World Economic Forum in collaboration with CPP Investments Insights Institute in June 2024, provides a strategic guide for investors to promote the adoption of Responsible Artificial Intelligence. The report emphasizes that AI's rapid integration across industries brings both opportunities and risks, making the implementation of responsible AI practices an imperative.
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Measuring what matters: How to assess AI’s environmental impact
This report from the International Telecommunication Union (the UN agency behind the "AI for Good" initiative) synthesizes key findings from a diverse range of sources, including academic literature, corporate sustainability initiatives, and emerging environmental tracking tools. Collectively, these documents provide a broad overview of current methodologies for evaluating the environmental impacts of artificial intelligence systems.
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The essentials of AI and ESG
Clarity AI is a leading sustainability Fintech company, leveraging AI-powered tools to provide environmental and social insights to investors and corporates. This brief paper provides a clear summary of AI potential for sustainable investors, but also of its risks. It also includes succint recommendations on potential mitigation measures.
moreRegulatory-related materials
OECD - Regulatory approaches to Artificial Intelligence
This OECD report examines the policy approaches to Artificial Intelligence (AI) in finance, based on the results of a survey of 49 OECD and non-OECD jurisdictions. The report provides information on current and potential use cases of AI in finance and some of the risks observed by survey respondents and provides a stocktake of the policy frameworks applicable to the use of AI in finance in different forms.
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Overview of AI regulation in Switzerland
So far, there has been no overarching AI-specific legislation in Switzerland . The Federal Council has commissioned the Department of the Environment and the Department of Foreign Affairs to draw up this overview of the possible regulation of AI, which will serve as a basis for the Federal Council's decision on future legislative steps. The overview (January 2025) defines three overarching objectives that should be fulfilled by Swiss AI regulation: (1) strengthening Switzerland as an innovation location, (2) safeguarding the protection of fundamental rights, including economic freedom, and (3) strengthening public trust in AI.
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FINMA Guidance - Governance and risk management when using AI
The use of AI in the financial market is increasing. For supervised institutions, this is associated with both opportunities and risks. To date, there is no AI-specific legislation in Switzerland. In this guidance (December 2024), FINMA draws attention to the corresponding risks and the need to adequately identify, limit and control these risks.
moreInternational Organization of Securities Commissions (IOSCO), Artificial Intelligence in Capital Markets: Use Cases, Risks, and Challenges, 2025, https://www.iosco.org/library/pubdocs/pdf/IOSCOPD788.pdf
Bank for International Settlements (BIS), The use of artificial intelligence for policy purposes, 2025, https://www.bis.org/publ/othp100.pdf
FINMA, Guidance 07/2026: Guidance on quantum computing, 2026, https://www.finma.ch/en/news/2026/07/20260709-mm-am-05-26/
FINMA, Artificial intelligence: FINMA sets out its supervisory expectations, 2024, https://www.finma.ch/en/documentation/dossier/dossier-fintech/kuenstliche-intelligenz/
European Union, Regulation (EU) 2024/1689 (Artificial Intelligence Act), 2024, https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
European Banking Authority (EBA), Special topic – Artificial intelligence, 2024, https://www.eba.europa.eu/publications-and-media/publications/special-topic-artificial-intelligence
Bank for International Settlements (BIS), Accelerated Data Science, AI and GeoAI for Sustainable Finance in Central Banking and Supervision, 2024, https://www.bis.org/bcbs/publ/d560.htm
Bank for International Settlements (BIS), Enabling climate risk analysis using generative AI, 2024, https://www.bis.org/publ/othp84.htm
Bank for International Settlements (BIS) Innovation Hub, Project Gaia: enabling climate risk analysis , 2024, https://www.bis.org/about/bisih/topics/suptech_regtech/gaia.htm
World Economic Forum (WEF), Responsible AI Playbook for Investors, 2024, https://www.weforum.org/publications/responsible-ai-playbook-for-investors/
United Nations, Artificial intelligence (AI), 2024, https://www.un.org/en/global-issues/artificial-intelligence
National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023, https://www.nist.gov/itl/ai-risk-management-framework
Any suggestion, comment or question? Reach out to Romain Leroy-Castillo, Director & Artificial Intelligence lead at SSF
