Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks

ArXiv ID: 2404.17369 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Nature-related Risks, Artificial Intelligence (AI), Sustainable Finance, ESG, Supply Chain Analysis, Sustainable Finance / Corporates

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers
  • Why: The paper outlines conceptual AI frameworks and probabilistic models for nature-related risk assessment but lacks detailed mathematical derivations or statistical backtesting. It discusses data challenges and proposes methods but does not present empirical results, datasets, or code, focusing more on strategic applications than empirical validation.
  flowchart TD
    A["Research Goal: Assess AI potential for spatially sensitive<br>nature-related financial risk assessment"] --> B["Data & Inputs"]
    B --> C["Key Methodology Steps"]
    C --> D["Computational Processes"]
    D --> E["Key Findings & Outcomes"]

    B --> B1["Satellite & Remote Sensing Data"]
    B --> B2["Financial & ESG Data"]
    B --> B3["Supply Chain & Geospatial Data"]

    C --> C1["Use Case 1: Brazil Beef<br>Greening Finance"]
    C --> C2["Use Case 2: UK Water Utility<br>Financing Green"]

    D --> D1["AI Techniques Applied"]
    D1 --> D1a["Data Gap Filling"]
    D1 --> D1b["Uncertainty Estimation"]
    D1 --> D1c["Time Series Analysis"]
    D1 --> D1d["Real-time Monitoring"]

    E --> E1["Scalable AI Frameworks"]
    E --> E2["Risk Assessment Models"]
    E --> E3["Actionable Insights for:<br>- Financial Institutions<br>- ESG Data Providers<br>- TNFD & Policymakers"]