AI-Driven Cash Flow Forecasting in ERP Systems: Integrating Economic Indicators and Real-Time Transaction Data Using LSTM-Based Time-Series Models

Abstract

Maintaining financial stability and strategic planning in companies depends on cash flow projections. Often relying on static spreadsheets and historical data, traditional approaches might not precisely reflect real-time financial situations. This study presents an AI-driven cash flow forecasting method included into Enterprise Resource Planning (ERP) systems. Combining real-time transaction data with outside economic indicators—such as inflation rates, interest rates, commodity prices, and geopolitical sentiment—helps companies to create more accurate and responsive cash flow models. This paper offers a unique AI-driven forecasting system that combines real-time ERP transaction data with external economic indicators—including inflation rates, interest trends, commodity prices, prices and geopolitical sentiment—to generate responsive, context-aware cash flow projections. Using a hybrid modeling strategy that includes LSTM-based deep learning, ARIMA time-series models, and ensemble machine learning algorithms (e.g., XGBoost), the suggested system adjusts constantly to internal and external financial dynamics. Our hybrid forecasting system combines time-series models, ensemble learning, and economic feature engineering inside cloud-native ERP platforms (e.g., Oracle Fusion Cloud). We show how artificial intelligence can dynamically predict cash inflows and outflows, early risk identification, and treasury operations support in maximizing liquidity situations. Simulations and case studies reveal notable decreases in working capital inefficiencies and up to 30% increase in forecast accuracy.