Deep learning-driven enterprise risk management and intelligent forecasting model construction

With the increasingly complex risk environment faced by enterprises, it is very important to build an accurate risk intelligent prediction model. In order to improve the level of risk management, this article focuses on building an enterprise risk intelligent prediction model driven by deep learning (DL). By using the method of literature research, this article sorts out the relevant theories, and with the help of empirical analysis, collects more than 5,000 multi-source data covering finance, market and operation of many enterprises in five years. After data cleaning and feature engineering pretreatment, LSTM algorithm is selected to build the model. In the process of model experiment verification, the model based on LSTM is compared with traditional logistic regression, SVM and simple neural network MLP model. The results show that the accuracy of the model based on LSTM is 94.5%, the recall rate is 88.9%, and the mean square error (MSE) is as low as 0.08. Compared with logistic regression, support vector machine (SVM) and multi-layer perceptron (MLP), it has obvious advantages. This study confirms that it is feasible for DL to be used in enterprise risk intelligent prediction, and the established model provides effective support for enterprise risk management, helps it to warn and deal with risks in advance, and enhances its risk coping ability.