Assessing machine learning strategy approaches for early warning of liquidity risk

Artificial intelligence (AI) and machine learning (ML) have been increasingly adopted in the banking sector due to their ability to analyze large-scale datasets, process complex variables, and uncover hidden patterns, especially in the context of liquidity risk, posing a significant challenge for commercial banks. This study contributes to the field by conducting a comprehensive evaluation of several widely used early warning models, such as least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and extreme gradient boosting (XGBoost), to identify the most suitable approach for forecasting liquidity risk in Vietnamese commercial banks (VCBs) based on VCBs data over the period of 2014–2023. By pinpointing key indicators associated with liquidity crises, these models can assist banks and regulatory authorities in implementing timely preventive measures and enhancing risk management strategies. As a result, the RF model outperforms other methods in identifying possible liquidity crises, according to the empirical results, with an accuracy rate of 99.8 percent. These findings provide bank managers and policymakers with a powerful tool for timely preventive measures, thereby enhancing the resilience and stability of the financial system.