Machine Learning for Sustainable Financial Systems: Assessing Corporate Resilience and Default Risk

Wei Zhao

doi:10.62177/amit.v1i6.849

Authors

Wei Zhao Turība University

DOI:

https://doi.org/10.62177/amit.v1i6.849

Keywords:

Machine Learning, Sustainable Finance, Corporate Resilience, Default Risk, Explainable AI, XGBoost

Abstract

This study explores how financial risk indicators influence corporate resilience and sustainability under market uncertainty. Using panel data of Chinese listed firms from 2010 to 2022, we develop machine learning models—including Random Forest, XGBoost, and Neural Networks—to evaluate firm-level default probability and resilience capacity. Unlike traditional linear models, our approach captures asymmetric and nonlinear responses between Distance to Default (DD) and Expected Default Frequency (EDF). The results reveal that financial fragility rises sharply when DD declines below critical thresholds, highlighting the need for resilience-oriented financial supervision. XGBoost achieves the best predictive performance, while Random Forest provides interpretability through feature importance and partial dependence analysis. The study contributes to sustainable finance by linking explainable AI with early-warning systems, offering data-driven tools for promoting financial stability and long-term sustainability in emerging markets.

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