Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
<|reference_start|>Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks: There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components' relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners.<|reference_end|>
@article{reece2024assessing,
    title={Assessing the Potential of AI for Spatially Sensitive Nature-Related
  Financial Risks},
    author={Steven Reece, Emma O'Donnell, Felicia Liu, Joanna Wolstenholme, Frida
  Arriaga, Giacomo Ascenzi and Richard Pywell},
    journal={arXiv preprint arXiv:2404.17369},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17369},
    primaryClass={q-fin.CP cs.AI}
}