Trustworthy App Detection in Saudi Mobile Finance: Bridging Deep Learning and Interpretability

Understanding user trust in mobile financial applications is crucial as these platforms increasingly shape how users in Saudi Arabia manage finances and engage with digital banking. However, existing deep learning–based detection models often function as black boxes, offering limited interpretability, while traditional machine learning models, though more transparent, fail to capture complex interactions between permissions, reviews, and user behaviors. To address this gap, we propose Trustworthy App Detection for Saudi Arabia (TAD-Saudi) - a novel framework for interpretable and behavior-aware trust evaluation. The frame-work integrates the representational power of deep learning with the explainability of simpler models, enabling both global and local interpretation of trust-related features. Experimental results show that TAD-Saudi outperforms traditional baselines across multiple models. Moreover, the analysis reveals that users may continue to trust applications requesting sensitive permissions, particularly when these apps have high ratings or positive reviews.

@article{Alharbi2026,
  title     = {Trustworthy App Detection in Saudi Mobile Finance: Bridging Deep Learning and Interpretability},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Raed Alharbi and Maryam Alghamdi},
  doi       = {10.14569/IJACSA.2026.0170594},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170594}
}