An Invariant and Balanced Deep Learning Approach for Financial Risk Assessment

Abstract

Financial risk assessment is a fundamental process in banking, insurance, and investment sectors, enabling institutions to quantify and manage exposure to credit, operational, and market risks. Traditional financial risk models rely on statistical learning techniques, such as logistic regression and decision trees, which are limited in handling non-linear financial dependencies, class imbalance, and distributional biases. Recent advances in deep learning (DL) and artificial intelligence (AI) have introduced more powerful models capable of capturing complex risk patterns and improving prediction accuracy. However, DL models often suffer from data imbalance issues and lack of invariance across economic conditions, leading to biased and inconsistent financial risk predictions.

This study proposes an invariant and balanced DL-based financial risk assessment framework, integrating graph neural networks (GNNs) for relational financial modeling, adversarial domain adaptation for bias mitigation, and cost-sensitive learning techniques for class imbalance correction. The model enhances risk prediction accuracy while ensuring fairness, robustness, and generalizability across different financial environments. Additionally, an explainability layer is incorporated to improve regulatory transparency and model interpretability.

Experiments on real-world financial datasets demonstrate that the proposed framework outperforms traditional financial risk models, achieving higher recall for high-risk entities, improved invariance across economic shifts, and reduced disparities in risk classification. The findings highlight the potential of DL-based risk assessment systems to offer more balanced, fair, and adaptive risk management strategies, ensuring more reliable financial decision-making in banking and investment sectors.