A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P105Keywords:
Liquidity Risk Management, Artificial Intelligence, Financial Forecasting, Risk Analytics, Financial TechnologyAbstract
In financial risk management, one of the main concerns has been the risk of liquidity, particularly in the contemporary volatile, complex, and highly interconnected markets, where the conventional models have proven to be inadequate. The novel role of artificial intelligence (AI), including machine learning (ML), deep learning (DL), and reinforcement learning (RL), in altering liquidity risk management procedures is examined in this paper. AI methods provide new opportunities to liquidity forecasting, stress testing, behavioral modeling, and the identification of early warning signals, as they allow processing large and heterogeneous volumes of data. This paper explains the major AI approaches, how they are used in financial organizations. It assesses the existing drawbacks related to explainability of models, regulatory compliance, data standards and compatibility with older systems. Additionally, it highlights the importance of explainable AI (XAI), hybrid AI systems, and collaborative systems involving data scientists, regulators, and financial professionals. The survey offers an extensive basis for future studies and practical implementation to reduce the gap between theoretical developments and real-world use of AI in liquidity risk management
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