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Machine Learning for Financial Risk Management: A Data-Driven Approach

Author(s)

Zhihan Liu*

Affiliation(s)

School of Business, Liaocheng University, Liaocheng, China *Corresponding Author

Abstract

The application of machine learning (ML) in financial risk management has begun to build the new model of transforming quantitative finance and improving traditional statistical methods and actuarial techniques. This study proposes a full data-driven framework based on ensemble learning, deep sequential models, and explainable artificial intelligence (XAI) technology to deal with the primary types of financial risks, namely credit risk, market risk, and systemic risk. Based on the corpus of high-quality empirical research at the forefront in recent years (2020-2025), this paper builds a system pipeline to solve problems such as class imbalance in multi-source data collection; builds a gradient boosting and hybrid neural-econometric model framework; and subsequently performs post-processing explainability analysis using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). According to the reference and actual financial data sets, among all combinations of methods, both XGBoost and Random Forest (RF) have achieved a relatively high Area Under the Curve (AUC) value, and the hybrid Long Short-Term Memory (LSTM) - Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model is better than the single model for volatility prediction. The application of XAI tools can help meet the more stringent demands of regulation, such as the EU's Artificial Intelligence Act, for explainability. Research conclusions will help enhance the public's understanding of data-driven risk management and offer operation Methodological reference for industry practitioners and researchers working in regulated financial environments.

Keywords

Machine Learning; Financial Risk Management; Credit Risk; Volatility Forecasting; Systemic Risk; Explainable AI; Ensemble Learning; Data-Driven Approach

References

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