Neural Network Models for Financial Forecasting within ERP Platforms

Accurate financial forecasting is critical for strategic decision-making within Enterprise Resource Planning (ERP) platforms. Traditional statistical models often fail to capture the complex, non-linear patterns present in ERP-generated financial data. This study investigates the application of neural network models specifically feedforward neural networks, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks for financial forecasting within ERP systems. Using historical data from real-world ERP financial modules, I develop and evaluate models based on forecasting accuracy, computational efficiency, and scalability. My results show that neural networks, particularly LSTM models, significantly outperform conventional methods in capturing temporal dependencies and providing more reliable forecasts. The paper also presents a practical framework for integrating these models into ERP environments, considering factors such as data preprocessing, system architecture, and deployment strategies. I address challenges such as data sparsity, real-time processing requirements, and model interpretability within enterprise settings. This research contributes a scalable and adaptable approach for enhancing financial analytics in ERP systems through artificial intelligence, offering actionable insights for both researchers and enterprise stakeholders. My findings encourage broader adoption of machine learning techniques for enterprise financial management and highlight future directions for integrating advanced AI models within ERP infrastructures.