 Drivers of bank stability in Vietnam's emerging economy: an advanced machine learning, regularization and explainable AI approachThuy Tu Pham Journal of Economic Studies, 2025, vol. 53, issue 5, 975-994 Abstract: PurposeThis study aims to investigate the key drivers of bank stability in Vietnam's emerging economy, offering a robust, data-driven framework that integrates advanced machine learning (ML), regularization techniques and explainable artificial intelligence (XAI) to address challenges in financial risk modeling and regulatory transparency.Design/methodology/approachUsing bank-level data from 2010 to 2023, this study employs Ridge, Lasso and Elastic Net regression to manage multicollinearity and identify relevant predictors. Gradient boosting with ridge regularization, optimized via particle swarm optimization, achieves superior predictive accuracy (R2 = 96.03%). SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are applied to interpret the model's outputs, revealing both global and local effects of explanatory variables.FindingsThe model attains a strong explanatory power (R2 = 96.03%), affirming the validity of the hybrid ML-XAI approach. Core drivers of banking stability – lag_ZSCORE, foreign bank presence, return on equity, equity ratio and macroeconomic factors – are consistently identified by SHAP and LIME. The integration of interpretable artificial intelligence (AI) not only enhances predictive accuracy but also delivers actionable insights, offering meaningful guidance for financial stability in emerging markets.Research limitations/implicationsWhile the model demonstrates strong performance within Vietnam's banking sector, its applicability may vary in different regulatory or macroeconomic environments. The study focuses on supervised learning and structured data; future research could explore unsupervised methods or unstructured sources like textual financial disclosures. Additionally, while SHAP and LIME provide interpretability, they do not guarantee causal inference. Nevertheless, the research lays a solid methodological foundation for future cross-country comparisons, particularly in the Association of Southeast Asian Nations, and encourages the integration of XAI into financial stability frameworks globally.Practical implicationsThis study equips financial regulators, central banks and policymakers with interpretable and high-performing tools to monitor and enhance banking stability. By clearly identifying and quantifying key stability drivers, it enables targeted, data-informed interventions and policy adjustments. Banks can also utilize these insights to optimize risk management, capital allocation and strategic planning. The transparent AI methods ensure trust in the decision-support system, promoting broader adoption in regulatory environments that demand both accuracy and explainability.Originality/valueThis is the first study in Vietnam to fuse ML, regularization and XAI for bank stability analysis. It not only enhances predictive power but also ensures model transparency, crucial for policymaking and Basel III compliance. The methodological innovation offers a replicable blueprint for financial risk assessment in emerging markets. Keywords: Bank stability; Machine learning; Regularization; Explainable AI (XAI) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eme:jespps:jes-04-2025-0237 Access Statistics for this article Journal of Economic Studies is currently edited by Prof Mohsen Bahmani-Oskooee More articles in Journal of Economic Studies from Emerald Group Publishing Limited |
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