Paper: arXiv 2412.18222

Authors: Yuhan Wang, Zhen Xu, Yue Yao, Jinsong Liu, Jiating Lin

Abstract

With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these methods have certain limitations in processing complex data and capturing potential risk patterns. To this end, this paper proposes a deep learning model based on the combination of convolutional neural networks (CNN) and Transformer for credit user default prediction. The model combines the advantages of CNN in local feature extraction with the ability of Transformer in global dependency modeling, effectively improving the accuracy and robustness of credit default prediction. Through experiments on public credit default datasets, the results show that the CNN+Transformer model outperforms traditional machine learning models, such as random forests and XGBoost, in multiple evaluation indicators such as accuracy, AUC, and KS value, demonstrating its powerful ability in complex financial data modeling. Further experimental analysis shows that appropriate optimizer selection and learning rate adjustment play a vital role in improving model performance. In addition, the ablation experiment of the model verifies the advantages of the combination of CNN and Transformer and proves the complementarity of the two in credit default prediction. This study provides a new idea for credit default prediction and provides strong support for risk assessment and intelligent decision-making in the financial field. Future research can further improve the prediction effect and generalization ability by introducing more unstructured data and improving the model architecture.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies a standard hybrid CNN-Transformer architecture to the well-known ‘Give Me Some Credit’ dataset. The mathematical formulation consists of elementary, standard deep learning equations without custom theoretical derivations, and the empirical section suffers from incomplete results text in the provided transcript.

Research Flowchart

  flowchart TD
    A[Goal: Enhance Credit Default Prediction Accuracy] --> B[(Public Credit Default Datasets)]
    B --> C[Model Architecture: Hybrid CNN + Transformer]
    C --> D[Training & Tuning: Optimizers & Learning Rate Adjustment]
    D --> E[Ablation Studies & Comparative Analysis vs. RF/XGBoost]
    E --> F[Outcomes: Superior Performance in Accuracy, AUC, and KS Value]