Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector

TL;DR

The paper addresses the environmental risk of AI in the banking sector and argues for embedding AI carbon footprints in risk management frameworks to meet sustainability goals and regulatory requirements. It outlines a structured approach to identify, measure, and mitigate AI-related emissions across the model lifecycle, aided by carbon accounting tools such as the GHG Protocol Toolkit and OpenLCA. Regulatory alignment with CSRD, CSDDD, the EU AI Act, and SS1/23 is mapped to RMF practices, with concrete mitigation strategies including energy-efficient modeling, green cloud computing, and lifecycle management. The work also highlights cutting-edge efficiency approaches (OLMoE, OneGen, FSPAD, MemoRAG, Agentic RAG) that can reduce emissions without sacrificing performance, offering banks a practical path to sustainable AI adoption.

Abstract

This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes increasingly central to banking operations, its energy-intensive processes contribute significantly to carbon emissions, posing environmental, regulatory, and reputational risks. Regulatory frameworks such as the EU AI Act, Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the Prudential Regulation Authority's SS1/23 are driving banks to incorporate environmental considerations into their AI model governance. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework and the Agentic RAG framework, offer more efficient and dynamic AI models, reducing their carbon footprint without compromising performance. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint within their RMFs, including adopting energy-efficient models, utilising green cloud computing, and implementing lifecycle management.