<![CDATA[Liquidity risk has emerged as a critical concern for financial institutions due to increasing market volatility, regulatory scrutiny, and the growing complexity of global financial systems. Traditional liquidity risk management approaches, which rely on static assumptions and low-frequency data, are often inadequate for capturing rapid intraday fluctuations in cash flows and funding requirements. This paper explores the application of machine learning techniques combined with big data architectures to enhance intraday liquidity prediction and risk modeling. The study presents a data-driven framework that leverages high-frequency transactional data, market indicators, and behavioral patterns to forecast liquidity positions in near real time. Advanced machine learning models, including ensemble methods and deep learning architecture such as Long Short-Term Memory (LSTM) networks are evaluated for their ability to capture nonlinear dependencies and temporal dynamics inherent in liquidity flows. The proposed approach integrates scalable big data technologies to support real-time ingestion, processing, and predictive analytics. Results demonstrate that machine learning-based models significantly outperform traditional methods in forecasting accuracy and responsiveness to market stress conditions. The paper also discusses practical implementation considerations, including model interpretability, regulatory compliance, and integration with enterprise treasury systems. By enabling proactive liquidity management and early detection of stress scenarios, the proposed framework offers substantial improvements in financial resilience and operational efficiency for modern banking institutions.]]>
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