ML-Based Financial Forecasting in ERP: Improving Budgeting Accuracy with Long Short-Term Memory Networks

This research examines the feasibility of using LSTM networks for financial prediction in frameworks such as the ERP system with an objective of enhancing budgetary prediction. Simple forecasting techniques do not take into account certain dynamics within the data that are non-linear, hence result in small forecast accuracy. Autoencoders can be employed to extract such features and LSTM networks capable of handling sequential data and long term dependencies could be used to anticipate the occurrence of emergencies. The process includes data culling that is composed of extensive datasets, data pre-processing, LSTM model development and training, and ERP system implementation. The findings illustrate that by extending the number of timesteps in LSTM models, it offers more reliability compared to conventional methods that help to improve decision-making and forecasting in finance. It is possible to upgrade them from time to time and make the models as accurate and as current as possible. These specific findings corroborate the overall proposition that discrete advancements in ML algorithms can converge to radically reshape a key area of ERP systems – financial forecasting.