Research on a Deep Learning-Based Dynamic Credit Risk Assessment Model for Enterprises

With the growing demand for corporate financing, credit risk assessment has become a crucial area of research in the financial sector, particularly given the complexities of today's dynamic market environment. Traditional valuation methods often lack transparency and efficiency, and are riddled with inaccuracies that need to change for some serious reform. In this study, we propose a dynamic credit risk assessment model based on deep learning using Long Short-Term Memory Network (LSTM). Significant efforts have been made to overcome these limitations in innovation technology and technical measures, so much so that the proposed model can capture long-term correlations between risk factor variables which extends well beyond boundaries of time and space. Building blocks consist of models based on deep learning with certain improvements applied to these popular models. The classical LSTM model is improved at multiple scales by layering and combining financial time series data feature information in this way. This approach incorporates diverse data sources—macroeconomic indicators, industry-specific risk factors, and corporate financial statements—into the LSTM's input layer. Subsequently, we process these inputs using an Adaptive Convolutional Layer (ACL), and as a result patterns over different time-scales are captured far more effectively by our model. In order to further improve forecast accuracy, it is also a dynamic weighted ensemble based on attention mechanism. This design causes the model to adjust weights in real-time, allowing for adaptive targeting of different time periods in historical data.