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All Journal Journal of Applied Data Sciences Journal of Digital Market and Digital Currency
Almasre, Miada
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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An Explainable Credit Card Fraud Detection Model using Machine Learning and Deep Learning Approaches Alkhozae, Mona; Almasre, Miada; Almakky, Abeer; Alhebshi, Reemah M.; Alamri, Amani; Hakami, Widad; Alshahrani, Lamia
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.962

Abstract

This study proposes an adaptive, interpretable real-time fraud detection and prevention system designed for high-risk financial environments, capable of processing over 1.6 million imbalanced credit card transactions with low latency. The objective is to build a unified framework that integrates predictive accuracy, explainability, and adaptability. The methodology follows four phases: exploratory data analysis to reveal structural and behavioral fraud patterns, feature engineering with domain-informed attributes and ADASYN oversampling to mitigate the 1:174 imbalance, training of multiple models (XGBoost, LightGBM, Random Forest, Gradient Boosting, and MLP), and an ensemble architecture evaluated with SHAP-based explainability. The system introduces three key contributions: stability-aware SHAP caching that reduces explanation latency to 41.2 ms, reinforcement learning–based threshold tuning that dynamically adapts to evolving fraud patterns, and out-of-distribution detection to enhance resilience against data drift. Results demonstrate strong performance, with XGBoost achieving 99.86% accuracy, 96.36% precision, 80.59% recall, F1-score of 0.878, and ROC-AUC of 0.9988, outperforming other models. The full system attained 93.2% accuracy, 90.2% F1-score, and 96.1% AUC at the system level, successfully blocking 91% of fraudulent transactions while maintaining a false positive rate of 7.8%. Novelty lies in combining explainability and adaptivity in a production-ready architecture, where reinforcement learning enables continuous threshold self-regulation and SHAP stability analysis validates interpretability across models. These findings show that high fraud detection accuracy and transparency are not mutually exclusive, offering a scalable blueprint for financial institutions and other critical domains requiring real-time, explainable, and adaptive decision-making.
Sentiment Analysis of Mobile Legends Play Store Reviews Using Support Vector Machine and Naive Bayes Alkhoze, Mona; Almasre, Miada
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.44

Abstract

This study applies sentiment analysis to Mobile Legends Play Store reviews to classify user feedback as positive, negative, or neutral, offering insights into the factors influencing user satisfaction. Utilizing machine learning models—Naive Bayes and Support Vector Machine (SVM)—user sentiment is evaluated, and key themes in user feedback are identified. Both models demonstrate high accuracy, with SVM slightly outperforming Naive Bayes. Specifically, the SVM model records an accuracy of 84.95%, a precision of 81.76%, and an F1-score of 83.31%, while Naive Bayes achieves an accuracy of 84.10%, a precision of 82.09%, and an F1-score of 82.57%. This classification highlights a predominance of positive reviews, revealing players’ appreciation for the game's graphics and gameplay. In contrast, negative reviews expose common frustrations related to lag and technical issues, indicating areas for potential improvement. The analysis also uncovers the challenge of accurately classifying neutral sentiments due to the informal language and slang found in reviews written in Bahasa Indonesia. Future studies could address this by incorporating advanced NLP techniques, such as word embeddings or deep learning models, to better capture linguistic nuances. Overall, this research provides actionable insights for game developers, enabling them to prioritize updates and feature enhancements that align with player preferences and feedback trends.
Co-Authors Alamri, Amani Alhebshi, Reemah M. Alkhozae, Mona Alkhoze, Mona Almakky, Abeer Alshahrani, Lamia Hakami, Widad