Research on Personal Credit Risk Assessment Based on Deep Belief Network

As the demand for personal credit risk assessment continues to grow, traditional credit scoring methods are increasingly inadequate in addressing the complexities of multidimensional data and information within modern financial systems. To tackle this challenge, this paper proposes a hybrid model combining Deep Belief Networks (DBN) and Random Forest (RF) to enhance the accuracy and robustness of personal credit risk evaluation. Initially, Deep Belief Networks are employed to learn features from customers' historical credit data, effectively capturing multi-layered nonlinear features. Subsequently, the ensemble learning advantages of Random Forest are utilized to classify the learned features, further strengthening the model's generalization ability and stability. Additionally, the paper introduces regularization strategies and adversarial training mechanisms to prevent model overfitting and improve its adaptability to noisy data and distribution shifts. Experimental results demonstrate that the proposed hybrid model outperforms traditional credit assessment methods on multiple public datasets, significantly improving the accuracy of credit risk prediction.