Article
CASH MANAGEMENT SYSTEM
Cash management is a critical function within financial institutions, ensuring optimal liquidity, efficient fund allocation, and secure transaction processing. Traditional cash management systems often rely on static rule-based frameworks and manual interventions, which limit their ability to handle real-time financial complexities, forecast cash flows accurately, and detect anomalies or fraud. With the increasing volume, velocity, and variety of financial data, there is a growing need for intelligent systems that can adapt, predict, and automate cash management processes. This study explores how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can be leveraged to build smarter, data-driven cash management systems. AI algorithms are used for real-time decision-making in cash forecasting, liquidity risk assessment, and transaction prioritization. ML models, such as Random Forest and Gradient Boosting, are employed to predict cash inflows and outflows based on historical patterns, business cycles, and external economic indicators. DL models, particularly Long Short-Term Memory (LSTM) networks, are utilized for sequence prediction in time-series financial data, enhancing the accuracy of cash flow forecasting. Additionally, anomaly detection techniques are integrated to identify potential fraud or operational errors, ensuring system integrity and compliance.By combining these intelligent technologies, the proposed system enhances operational efficiency, reduces human error, and improves financial planning accuracy. This AIdriven framework represents a transformative step toward building fully automated, adaptive, and secure cash management systems that align with the dynamic needs of modern financial institutions.
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