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Banks are facing increasing pressure to manage liquidity in real time as transaction volumes grow and financial environments become more complex. AI-powered liquidity management uses machine learning, predictive analytics, and automation to improve treasury operations and forecasting accuracy. In fact, 74% of treasurers are expanding AI adoption for liquidity forecasting and risk detection. By replacing static models with data-driven intelligence, banks can move toward faster, more accurate, and proactive liquidity decisions.