Machine Learning Systems Supporting Climate Related Financial Risk Reporting

Abstract

This paper introduces a novel, cross-disciplinary framework that integrates machine
learning with climate science to address the emerging challenge of climate-related financial
risk (CRFR) reporting. Unlike traditional financial risk models that treat climate factors
as 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 combination
of convolutional neural networks (CNNs) for processing spatial climate data from coupled
ocean-atmosphere models, long short-term memory (LSTM) networks for temporal financial series analysis, and a symbolic reasoning layer that encodes domain-specific knowledge
from climate economics and financial accounting standards. This integration allows for
the explicit modeling of non-linear, compound climate-physical risks—such as concurrent
heatwaves and droughts—and their cascading impacts on corporate asset valuations, supply chain resilience, and creditworthiness. We demonstrate the system’s application using
a 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 predicting
climate-driven value-at-risk (VaR) metrics, with a mean absolute error reduction of 32% in
a 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 prior
research by not merely applying ML to climate or finance separately but by architecting a
unified system that fundamentally redefines the problem formulation, treating climate and
financial data as a single, complex adaptive system. The findings offer financial institutions
a novel, robust tool for meeting evolving disclosure mandates and provide a foundational
architecture for next-generation environmental, social, and governance (ESG) analytics.