Article
CASH MANAGEMENT OF ANNUAL
Cash management plays a pivotal role in the financial health and operational stability of organizations. It involves the systematic planning, monitoring, and optimization of cash inflows and outflows to ensure sufficient liquidity while maximizing returns on idle cash. In the context of increasing financial complexity and volatility, traditional cash management approaches are no longer sufficient. This study explores the integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) into annual cash management systems to enhance efficiency, accuracy, and foresight. Using historical financial transaction data from annual records, the study leverages ML algorithms such as Random Forest, Decision Trees, and XGBoost to forecast future cash flow patterns. These models help identify peak expenditure periods, potential cash shortages, and optimal investment windows. Deep Learning models, particularly Recurrent Neural Networks (RNNs) and LSTMs (Long ShortTerm Memory), are used to capture time-dependent patterns and seasonal fluctuations in cash behavior. Furthermore, AI-driven dashboards and anomaly detection systems are introduced to alert financial managers in real time about irregularities in cash movements or deviations from forecasts. By combining AI, ML, and DL, this study provides a smart, data-driven framework for annual cash management, enabling organizations to make proactive and informed financial decisions. The results demonstrate that intelligent forecasting and automation significantly improve liquidity planning, reduce manual errors, and increase transparency in cash operations. This approach not only optimizes working capital but also strengthens overall financial control and risk management in modern institutions.
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