A Deep Learning Approach for Financial Risk Prediction in Enterprise Management Systems

Abstract

This study presents a novel deep learning-enabled financial risk prediction model for corporate management systems, which aims to promote proactive decision-making and business resilience. The complex nature of enterprise financial data makes it challenging for traditional statistical models to accurately represent these intricate, nonlinear relationships. We combine recurrent neural networks (RNNs) and attention mechanisms to capture the temporal dependencies while dynamically highlighting important financial indicators. The system learns from large, multivariate datasets that include financial transactions, operational metrics and external economic factors, which it uses to adaptive learn risk patterns with extreme accuracy. We also introduce explainable AI methods to enhance model interpretability and build trust among stakeholders. Experimental results show that our deep learning model significantly outperforms traditional machine learning baselines, including logistic regression and random forests, in financial distress events prediction, with higher precision, recall, and F1 score. This predictive ability enables businesses to detect risk exposure in the early stage, optimize resource allocation, and suppress possible losses. The framework is intended to complement existing installed enterprise resource planning (ERP) systems with real-time risk monitoring and decision support. In general, our work highlights the disruptive nature of deep learning for financial risk analytics and operational intelligence within the context of enterprise.