AI-Driven Solution for Instant Liquidity Risk Assessment in Financial Institutions

In the banking industry, where quick, precise risk assessment determines regulatory compliance and financial stability, liquidity risk still causes great worry.Emphasizing forecast accuracy, efficiency, and flexibility, this work investigates artificial intelligence (AI) application to improve real-time liquidity risk assessment.Using machine learning (ML) and deep learning (DL) approaches, we construct a model to offer dynamic risk insights by aggregating transactional data, market circumstances, and historical liquidity trends.Early detection of likely liquidity stress by the AI-driven approach not only provides a smart alternative for conventional static operations but also fits changing market patterns.Comparative study using traditional models shows significant improvements in reaction times and forecast accuracy, hence improving liquidity management choices.Emphasizing the possibilities of AI-based solutions for proactive and responsive liquidity risk assessment in banks, this study supports the expanding area of artificial intelligence in financial risk management.