Machine Learning Applications in Climate Related Financial Stress Testing

Abstract

This paper introduces a novel, hybrid machine learning framework for climate-related
financial stress testing (CRFST), addressing a critical gap in the integration of non-linear
climate dynamics with traditional financial risk models. Current approaches largely rely
on historical data and linear projections, failing to capture the complex, cascading impacts
of climate change on financial systems. Our methodology uniquely combines a QuantumInspired Neural Network (QINN) for modeling high-dimensional, non-linear relationships
between climate variables and asset prices, with a Bio-Inspired Optimization Algorithm
(BIOA) for stress scenario generation. The BIOA, modeled on adaptive immune system
response, evolves multi-hazard climate scenarios (e.g., concurrent heatwaves, floods, and
policy shocks) that are physically plausible yet financially severe, moving beyond simple
sensitivity analysis. We apply this framework to a synthetic dataset representing a diversified
global portfolio, simulating financial stress under a range of climate pathways. Results
demonstrate that our model identifies critical, non-linear tipping points in asset correlations
and valuation shocks that are missed by conventional Value-at-Risk and scenario analysis
models. Specifically, we find that climate physical and transition risks interact in a superlinear fashion, amplifying losses by 40-60