Predictive Analytics for Liquidity Risk Monitoring in Real-Time
DOI:
https://doi.org/10.63282/3050-922X.AECTIC-108Keywords:
Liquidity Risk, Predictive Analytics, Real-Time Monitoring, Machine Learning, Deep Learning, LSTM, Gradient Boosting, ARIMA, Time-Series Forecasting, Basel III, Liquidity Coverage Ratio (LCR), Net Stable Funding Ratio (NSFR), Stress Testing, Streaming Data Architecture, Apache Kafka, Big Data Analytics, Financial Risk Management, Intraday Liquidity, Anomaly Detection, Ensemble Models, Cloud-Native Solutions, Explainable AI (XAI), Generative AI, Blockchain IntegrationAbstract
Liquidity risk remains one of the most critical challenges for financial institutions, directly impacting their ability to meet short-term obligations and maintain regulatory compliance. Traditional liquidity risk monitoring methods rely heavily on static models and delayed reporting, which fail to provide timely insights into volatile markets. This paper introduces a predictive analytics framework for real- time liquidity risk monitoring, leveraging machine learning algorithms and streaming data architectures to forecast liquidity gaps and identify potential stress scenarios before they materialize. The proposed approach integrates time-series forecasting, advanced machine learning models such as Gradient Boosting and LSTM networks, and real-time data ingestion platforms to deliver actionable insights for treasury and risk management teams. A simulated case study demonstrates the effectiveness of predictive models compared to conventional approaches, highlighting improvements in accuracy, responsiveness, and operational resilience. The findings underscore the transformative potential of predictive analytics in enhancing liquidity risk management, reducing regulatory breaches, and strengthening financial stability
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