Deep Learning-Based Attention Mechanism Algorithm for Blockchain Credit Default Prediction

With the rise of internet finance and the increasing demand for personal credit risk management, accurate credit default prediction has become essential for financial institutions. Traditional models face limitations in handling complex and large-scale data, especially in the blockchain domain, which has emerged as a crucial technology for securing and processing financial transactions. This paper aims to improve the accuracy and generalization of blockchain-based credit default prediction models by optimizing deep learning algorithms with the Special Forces Algorithm (SFA) and attention mechanism (AM) networks. The study introduces a hybrid approach combining SFA with AM to optimize hyperparameters of the credit default prediction model. The model preprocesses blockchain credit data, extracts critical features such as user and loan information, and applies the SFA-AM algorithm to improve classification accuracy. Comparative analysis is conducted using other machine learning algorithms like XGBoost, LightGBM, and LSTM. Results: The SFA-AM model outperforms traditional models in key metrics, achieving higher precision (0.8289), recall (0.8075), F1 score (0.8180), and AUC value (0.9407). The model demonstrated better performance in identifying both default and non-default cases compared to other algorithms, with significant improvements in reducing misclassifications. The proposed SFA-AM model significantly enhances blockchain credit default prediction accuracy and generalization. While effective, the study acknowledges limitations in dataset diversity and model interpretability, suggesting future research could expand on these areas for more robust applications across different financial sectors.

@article{Lin2025,
  title     = {Deep Learning-Based Attention Mechanism Algorithm for Blockchain Credit Default Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wangke Lin and Yue Liu},
  doi       = {10.14569/IJACSA.2025.0160241},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160241}
}