Research on the Financing Risk Warning of Real Estate Enterprises Based on Recurrent Neural Network

This paper constructs an intelligent early warning system for financing mismatch risks in real estate enterprises based on intelligent technologies such as recurrent neural networks. First, it establishes a risk intelligence identification framework based on web crawling, text analysis, and grounded analysis techniques, serving as the foundational elements for constructing risk early warning indicators in two dimensions: financing term mismatch and financing structure mismatch. Subsequently, a recurrent neural network model is employed as the primary modeling technique, using the risk indicators of financing mismatch identified through intelligence as input layer information. It further categorizes the financing mismatch risks of real estate enterprises and implements intelligent warnings for these risks, focusing on both financing term mismatch and financing structure mismatch. The model employs the recurrent neural network to achieve this, and through the use of the Captum interpretability library, it derives relative importance scores for the financing mismatch risk indicators of real estate enterprises and constructs a comprehensive early warning index for financing mismatch risks. This allows for the identification of key impact paths related to financing mismatch risks and the localization of regional financial risks stemming from these impacts.