Model Development and Application of Machine Learning in Liquidity Risk Management for Financial Institutions

Historical and recent financial crises highlight the criticality of managing liquidity risk for staying financial steady. The traditional approaches are increasingly inadequate: overly simplistic, limited to linear statistical models and static assumptions, they fail to handle modern finance’s massive, high-dimensional structured data, missing non-linear dynamics and tail risks, leading to incomplete risk assessments This paper explores leveraging Machine Learning (ML) for dynamic, precise liquidity risk prediction. Moving beyond standard statistics to generate timelier, more accurate liquidity shortage early warnings. It employs tree-based ensembles (Random Forest, Gradient Boosting) to forecast fine-grained account-level dynamic cash flows, covering deposit run-offs and credit drawdowns. Additionally, it explores Long Shot-Term Memory (LSTM) time-series models for high-frequency intraday temporal dependencies, and the use of classification algorithms and Natural Language Processing (NLP) for resilient early warning systems. It further explores detecting early liquidity stress via market data, sentiment, and filings, plus key implementation requirements. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations （LIME）address the “black box” problem critical to regulatory and managerial trust. This will be discussed in detail. The paper argues that such rigorous vetting, investigation, and comparative data–representing prudent ML application–are no longer just a competitive tool, but a key advancement in financial institutions’ liquidity risk management, enabling proactivity, strategic agility, and resilience amid challenging economies.