Saudi Cultural Missions Theses & Dissertations
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Item Restricted Predicting Client Default Payments Using Machine Learning in Production Environment(Saudi Digital Library, 2025) Alanazi, Reem; LavendiniThis project investigates the application of machine learning techniques to predict client default payments in a credit card setting. Using a dataset of 30,000 Taiwanese clients, the study addresses the challenges of class imbalance, predictive accuracy, and fairness in credit risk assessment. An XGBoost model was developed and enhanced through feature engineering, resampling techniques (SMOTE/ADASYN), and class weighting to improve recall for defaulters while maintaining overall accuracy. Interpretability was achieved using SHAP values, providing transparency into model decisions. To mitigate demographic disparities, particularly across education levels, a fairness-constrained Random Forest was integrated into a two-stage cascade framework, reducing false positives while preserving high recall. The final cascade model achieved 84% accuracy, with 93% recall for non-defaulters and 53% recall for defaulters, significantly outperforming baseline benchmarks. Fairness audits revealed that education-based disparities could be reduced with minimal performance trade-offs, while age-based fairness was largely maintained. The project demonstrates a practical, interpretable, and ethically aware pipeline for credit default prediction, with deployment considerations and directions for future research in cost-sensitive learning, advanced fairness constraints, and real-time monitoring17 0Item Restricted Optimal 0−1 Loss Classification In Linear, Overlapping And Interpolation Settings(University of Birmingham, 2022-09-07) Alanazi, Reem; Max, LittleClassification problems represent a major subject in machine learning, and addressing them requires solving underlying optimization problems. Optimis- ing the 0–1 loss function is the natural pathway to address the classification problem. Because of the properties of 0–1 loss, which are that it is non-convex and non-differentiable, 0–1 loss is mathematically intractable and classified as non-deterministic polynomial-time hard (NP-hard). Consequently, the 0– 1 loss function has been replaced by surrogate loss functions that can be optimized more efficiently, where their optimal solution is guaranteed with respect to these surrogate losses. At the same time, these functions may not provide the same optimal solution as the 0–1 loss function. Indeed, the loss function used during the empirical risk minimization (ERM) is not the same loss function used in the evaluation; the mismatch of the loss functions leads to an approximate solution that may not be an ideal solution for 0–1 loss. Thus, an additional source of error is produced because of this replacement.46 0