<ul><li>This paper proposes a deep learning model combining convolutional neural networks (CNN) and Transformer for credit user default prediction.</li><li>The model combines the advantages of CNN in local feature extraction and Transformer in global dependency modeling.</li><li>Experimental results show that the CNN+Transformer model outperforms traditional machine learning models in accuracy, AUC, and KS value.</li><li>The study provides a new idea for credit default prediction and supports risk assessment and intelligent decision-making in the financial field.</li></ul>

Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications

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