Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications (2412.18222v1)

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.

• The paper introduces a hybrid CNN-Transformer model that boosts credit default prediction performance over traditional methods.
• It combines CNN’s local feature extraction with Transformer’s global dependency modeling, validated with superior accuracy, AUC, and KS scores.
• The integration shows robust performance across hyperparameters, highlighting its promise for reliable financial risk assessment in diverse scenarios.

Synergizing Convolutional Neural Networks and Transformers for Enhanced Credit Default Prediction

The research paper, "Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications," presents a hybrid deep learning approach for credit default prediction by integrating Convolutional Neural Networks (CNN) with Transformers. The model aims to overcome the limitations of traditional machine learning models, such as decision trees and random forests, in handling complex and nonlinear datasets typical in financial risk management scenarios.

The novelty of this paper lies in the combination of CNN's local feature extraction capabilities with the Transformer model's comprehensive global dependency modeling. The integration effectively enhances the accuracy and robustness of predicting users’ likelihood to default on credit obligations. This hybrid approach has been validated against public credit default datasets, demonstrating superior performance in terms of accuracy, AUC, and KS scores over well-established models such as random forests and XGBoost.

Methodological Framework

The proposed framework begins by preprocessing structured and unstructured data. CNN layers extract local features from structured data, such as user demographics and credit history, while text embeddings like Word2Vec prepare unstructured data, including user logs and descriptions, for subsequent feature extraction. Once the local features are captured, the model employs Transformer layers to model the long-term dependencies inherent in the processed data, using self-attention mechanisms for richer feature representation. Several convolutional, pooling, and fully connected layers facilitate further feature fusion and transformation.

The combination of CNN and Transformer output is subjected to a multi-layer perceptron (MLP), culminating in the calculation of default probabilities, optimized through a cross-entropy loss function.

Empirical Findings

The experimental analysis utilized the "Give Me Some Credit" dataset, containing structured features like user age, job type, and credit utilization rates, among others. The model's performance was compared against established techniques, revealing that the CNN+Transformer model attained an accuracy of 0.8197, an AUC of 0.7921, and a KS value of 0.4352—parameters that indicate a robust improvement over Random Forest (RF) and XGBoost models.

Key experimental insights highlighted the model's resilience to varying hyperparameters through learning rate and optimizer sensitivity analyses. The CNN+Transformer performance exhibited negligible deviation across different learning rates and optimization algorithms, underscoring its adaptability and robustness. Ablation studies further corroborated the superiority of the integrated CNN+Transformer model over isolated CNN or Transformer models, reflecting a significant enhancement in capturing credit risk patterns.

Implications and Future Directions

The synergy of CNN and Transformer models for credit default prediction holds substantial implications for financial risk assessment. Superior predictive accuracy can enable financial institutions to more reliably identify potential default risks and make informed decisions, thus improving asset quality and reducing bad debts. From a theoretical standpoint, the work exemplifies how hybrid models can leverage the strengths of distinct machine learning architectures to address complex predictive tasks.

Future investigations could explore the inclusion of additional unstructured data sources, such as social media activity, to further refine predictive accuracy and model generalization. Advancements in deep learning architectures and training methodologies could also provide avenues for enhancing model robustness against dynamically shifting financial markets.

Overall, the integration of CNN and Transformer models offers a promising avenue for advancing predictive modeling in the financial sector. This work sets a precedent for the continued incorporation and evolution of AI-driven credit risk assessment strategies, paving the way for more intelligent, data-informed decision-making processes.