Enterprise Economic Risk Early Warning Model Based on Deep Learning and Association Rule Mining

Abstract

In today's volatile and uncertain business landscape, enterprises encounter a myriad of economic risks that can significantly impact their performance and sustainability. Traditional risk management methodologies often fall short in effectively identifying and mitigating these risks in a timely manner. This paper presents an innovative Enterprise Economic Risk Early Warning Model that leverages the capabilities of deep learning and association rule mining to provide enterprises with proactive risk management strategies. The proposed model integrates advanced machine learning techniques to analyze vast and heterogeneous datasets encompassing financial indicators, market trends, and macroeconomic factors. By uncovering intricate patterns and relationships within the data, the model can identify subtle signals and early warning signs of emerging risks. Additionally, association rule mining techniques are employed to unveil hidden dependencies and associations among different risk factors, enhancing the model's accuracy and contextual understanding of the risk landscape. Through empirical validation and case studies, this paper demonstrates the efficacy and practical applicability of the proposed model in enabling enterprises to fortify their resilience against economic uncertainties and optimize decision-making processes for sustained growth and competitiveness.