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

arXiv: https://arxiv.org/abs/2410.01818 · HTML: https://mlsanity.com/paper/2410.01818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [OLMoE: Open Mixture-of-Experts Language Models](https://mlsanity.com/paper/2409.02060/) (2024)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation](https://mlsanity.com/paper/2409.05591/) (2024)
- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (2023)
- [Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation](https://mlsanity.com/paper/2408.15562/) (2024)

## Abstract (arXiv)

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.