AI Powered Real Time Analytics for Liquidity Risk Assessment in Banking Sector

In the banking sector, where regulatory compliance and financial stability rely on rapid, accurate risk assessment, liquidity risk still generates enormous concern. Emphasizing forecast accuracy, efficiency, and flexibility, this work explores artificial intelligence (AI) application to enhance real-time liquidity risk assessment. We develop a model to provide dynamic risk insights by combining transactional data, market conditions, and historical liquidity patterns using machine learning (ML) and deep learning (DL) techniques. Early identification of probable liquidity stress by the AI-driven technique not only offers a sophisticated solution for traditional static procedures but also matches evolving market trends. Comparative investigation utilizing conventional models reveals considerable gains in reaction times and prediction accuracy, therefore enabling better liquidity management decisions. This paper supports the growing domain of artificial intelligence in financial risk management by stressing the opportunities of AI-based solutions for proactive and responsive liquidity risk assessment in banks.

Keywords: Liquidity Risk Assessment, Artificial Intelligence, Real-Time Risk Management, Deep learning model, LSTM