A Novel Algorithm for Credit Default Prediction using TabNet

This paper aims to explore the application of TabNet model in predicting credit default rates. Loan default rate is a critical indicator for assessing the risk of financial institutions, and accurate prediction of loan default rates is crucial for financial risk management. Traditional statistical models have certain limitations in handling high-dimensional and non-linear data, while the TabNet model possesses strong learning capabilities and interpretability, making it suitable for complex financial data analysis. This study begins by introducing the background and importance of predicting loan default rates, and reviews the limitations of traditional models in this domain. The principles and characteristics of the TabNet model are then described in detail. The model, based on self-attention mechanism and sparse attention mechanism, is capable of automatically learning useful representations from a large number of features and exhibits good interpretability. The process of data collection and preprocessing, as well as the experimental settings of the model, are also presented. In the experimental section, a series of experiments were conducted using real loan datasets to evaluate the performance of the TabNet model in predicting loan default rates. Compared to traditional statistical models, the TabNet model achieved significant improvements in accuracy and generalization ability.