(untitled) (gjstudies, 2023)
This paper introduces a novel, cross-disciplinary framework that integrates machinelearning with climate science to address the emerging challenge of climate-related financialrisk (CRFR) reporting. Unlike traditional financial risk models that treat climate factorsas exogenous shocks, our methodology, termed the Climate-Finance Neural Architecture(CFNA), embeds high-resolution climate projections directly into financial forecasting models through a hybrid neural-symbolic approach. The CFNA leverages a unique combinationof convolutional neural networks (CNNs) for processing spatial climate data from coupledocean-atmosphere models, long short-term memory (LSTM) networks for temporal financial series analysis, and a symbolic reasoning layer that encodes domain-specific knowledgefrom climate economics and financial accounting standards. This integration allows forthe explicit modeling of non-linear, compound climate-physical risks—such as concurrentheatwaves and droughts—and their cascading impacts on corporate asset valuations, supply chain resilience, and creditworthiness. We demonstrate the system’s application usinga proprietary dataset linking historical financial statements of firms in agriculture, energy,and real estate to localized climate hazard indices. Our results show that the CFNA outperforms standard econometric models and isolated machine learning techniques in predictingclimate-driven value-at-risk (VaR) metrics, with a mean absolute error reduction of 32% ina five-year forward-looking scenario analysis. Furthermore, the model generates explainable,audit-ready reports that trace specific climate variables to financial line items, a critical requirement for regulatory compliance. This work represents a significant departure from priorresearch by not merely applying ML to climate or finance separately but by architecting aunified system that fundamentally redefines the problem formulation, treating climate andfinancial data as a single, complex adaptive system. The findings offer financial institutionsa novel, robust tool for meeting evolving disclosure mandates and provide a foundationalarchitecture for next-generation environmental, social, and governance (ESG) analytics.