Research on Credit Risk Prediction Technology Based on Deep Learning

The number of institutions engaged in credit business is increasing, the types of credit products are constantly enriched, and the credit balance is growing rapidly, which has led to an increasing demand for intelligent credit risk management. Pre-loan review is the most core stage in credit risk management, and a good pre-loan risk prediction model can minimize future risks. Some small and medium-sized banks still use traditional mathematical and statistical methods for risk prediction, which can neither meet the growing business needs of credit risk prediction nor guarantee the quality of prediction. Based on the research results of domestic and foreign scholars in the field of customer credit default prediction, this paper proposes a bank credit risk prediction management model based on a multi-level deep neural network to address the limitations of small and medium-sized banks using traditional methods based on mathematical statistics for risk prediction. Using the actual loan data of a commercial bank as experimental data, the simulation experimental results show that the model has a high prediction accuracy.