Machine Learning based Geospatial Sensitivity Analysis Tool (ML-GeoSAT) for Gross Exposure to Climate-Related Risks

点击翻译按钮获取中文摘要

ML-GeoSAT is a unique tool that will allow underwriters to measure the direct and indirect impacts of physical and transition risks on the entire value chain of a counterparty to determine exposure or vulnerability risks/levels. Particularly, ML-GeoSAT is suited for direct counterparty investments like extractive and mining companies (involved in direct extraction processes like cobalt and other mineral resources mining) and indirect counterparties like battery and electric vehicles manufacturers with transcontinental supply chain networks. For the direct counterparties, underwriters are interested in understanding how weather patterns and local climate-related policies can influence or inflict damage on these investments with or without mitigation (gross or net losses) while for the indirect counterparties, underwriters are interested in understanding how physical and transition risks on supply chain infrastructure can affect a counterparty's ability to service debts or meet other financial obligations.ML-GeoSAT is a disruptive approach to evaluating risks, especially for counterparties as it adopts a Natural Capital Analysis model in risk evaluation. ML-GeoSAT posits nature as capital stock and accordingly assesses how natural degradation negatively impacts a financial institution. This takes the form of a portfolio-level assessment that helps financial institutions identify their natural capital asset dependencies. It is usually carried out in four steps, identifying: (1) relevant geographies, sectors, borrowers and/or assets; (2) relevant natural capital assets (e.g., minerals, water, rubber etc.) that their directly or indirectly tied to counterparties; (3) potential natural and transition-related disruptions that could occur; and (4) geographies, sectors, borrowers and/or assets most at risk and its overall impact on the underwriters' portfolio risk.ML-GeoSAT will seek to achieve the following: (1) dynamically discretise counterparties portfolios by creating a logistics-based pathway of inputs from source to final products; (2) utilise geospatial data and advanced image recognition in evolving a physical risk framework (for real-time and predictive risk assessments) for each material hub; (3) leverage advanced natural language processing techniques in evolving a real-time knowledge-based framework (for digitising real-time climate policies and codifying a transition-risk assessment protocol) for each material-hub (region-specific); (4) integrating physical and transition risk assessments for each material hub into a comprehensive climate-risk assessment tool (CRAT) for real-time sensitivity analysis; (5) providing an intuitive visualisation platform with innovative financial metrics to convey real-time and future-based impairment for varying scenarios.