Research on Ai-Driven Liquidity Risk Prediction and Dynamic Management in the Banking Sector

Abstract

This study presents a meta-analysis of artificial intelligence (AI) applications in predicting and dynamically managing liquidity risk within the commercial banking sector. Drawing from the empirical evidence from peer-reviewed journals, industry reports, and case studies from the years 2015-2025, this research compares the performance of AI models (Long Short-Term Memory (LSTM), XGBoost, and Deep Q-Networks (DQN)) with traditional forecasting methods (ARIMA and Historical Simulation models). Results show that AI models always beat the traditional approach on forecasting accuracy (up to 99.3%), always better on root mean square error (RMSE), and predict with a much shorter lag. Real-world implementations in institutions like JPMorgan Chase and China Construction Bank reveal operational gains in transaction processing speed, cost efficiency, and early warning capabilities. The study further tests AI performance under stressful situations, such as the COVID-19 pandemic and regional banking turbulence, showing that it was resilient. Despite obvious benefits, challenges in legacy systems, ambiguity in the regulation, and interpretability of models in terms of accuracy act as barriers to widespread adoption. In conclusion, this paper states that AI provides a transformative edge for proactive, data-driven liquidity risk management, and banks should, therefore, invest in AI integration, which should complement any transparency and regulatory compliance issues.