Financial Forecasting and Budget Management Based on Machine Learning

Abstract

This article focuses on the application of machine learning technology in financial forecasting and budget management to improve the intelligence level and decision-making efficiency of enterprise financial management. Given the shortcomings of traditional financial models in terms of data processing capability, prediction accuracy, and response speed, an improved Sparse Denoising Autoencoder (SDAE) neural network is introduced as the core modeling tool. And by using interval discretization method to construct a robust financial prediction model to achieve anomaly smoothing. Through comparative experiments, the performance of the improved SDAE model was compared with standard SDAE and traditional backpropagation neural networks (BPNN). The results indicate that the improved SDAE outperforms the other two models in terms of prediction accuracy and stability, and can complete prediction tasks within milliseconds even when processing tens of thousands of records. In addition, system concurrency testing has shown that it has good scalability and resource scheduling capabilities, and can support multi departmental parallel usage scenarios.