Research on Enterprise Risk Prediction Model and Control Using Cloud Computing and Deep Learning

This paper proposes an enterprise risk prediction model combining cloud computing, Long Short-Term Memory (LSTM) and LightGBM, aiming to improve the real-time performance and accuracy of enterprise risk prediction. Firstly, the large-scale data processing ability of cloud computing platform was used to collect, clean and preprocess multi-source data from finance, market, industry and other sources. Then, LSTM networks are employed to capture the time series characteristics of enterprise risks, effectively extracting both long-term trends and short-term fluctuations in risk indicators through its memory units. After extracting these time-dependent features with LSTM, LightGBM (Light Gradient Boosting Machine) is utilized to model the non-time series data. LightGBM's efficient decision tree algorithm excels at handling large-scale datasets quickly, thereby enhancing the model's predictive performance. Finally, the model is deployed on a cloud computing platform, enabling real-time risk prediction and control. This approach not only improves the accuracy of enterprise risk prediction but also offers excellent scalability and flexibility, making it a robust solution for enterprise risk management.