Saudi Cultural Missions Theses & Dissertations
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Item Restricted Supervised Machine Learning. A Strategic Approach for Financial Fraud Detection(University of Nottingham, 2024-03) Bashehab, Omar Sami; Wang, HuamaoFinancial fraud is an increasingly concerning issue in the present day. The rapidly growing rate of fraudulent activities has led to significant financial losses for many stakeholders. Card-not-present (CNP) fraud has risen with the growth of digital sales. The same benefits attracting online banking and transactions have attracted fraudsters and cybercriminals. Consequently, the incentive for fraud detection for mitigating financial risk is evident. However, traditional detectors are outdated, and rule-based systems fail to keep up with the dynamic innovative methodologies of cybercriminals. Thus, the ML-based system is needed. However, various challenges exist within ML-based detectors. Firstly, datasets are typically highly imbalanced and secondly, a lack of real-world datasets makes research extremely difficult. To tackle these problems, different resampling methods such as RUS, ROS, SMOTE and a hybrid sampling approach (ROS + RUS) were used and evaluated. Furthermore, a novel dataset was used, augmented from an original PAYsim real-world synthetic data. Furthermore, predictive models such as Decision Tree, Logistic Regression, Random Forests, Support Vector Machine and (Gaussian) Naïve Bayes were used with the different resampling methods in a comparative approach. Finally, the importance of data preprocessing and feature engineering was explored and evaluated amongst the classifiers. The experimental results illustrate the Random Forest, with Grid Search CV optimisation and RUS as well as feature engineering performed the best. The methodological approach exhibited an increase in F1 score, True Positive Rate, Recall and Accuracy for the classifier. The final model outputted an F1 score of 69%, ROC-AUC of 88% and True Positive Rate (TPR) of 93%.78 0Item Restricted Credit Card Fraud Prediction Using Machine Learning Model(University of Essex, 2024-08) Alanazi, Mohammed; Walton, MichaelThe widespread adoption of credit cards has significantly increased the frequency of fraudulent activities. This has resulted in considerable financial losses for both consumers and financial institutions. As the use of credit cards continues to grow, the challenge of protecting transactions against unauthorized access has become more serious than ever. This research focuses on creating a solution using machine learning to accurately and effectively identify fraudulent credit card transactions. It addresses the issue of uneven transaction data by employing advanced methods such as logistic regression, XGBoost, LightGBM, and a hybrid model. The research involves thorough data preparation, model development, and careful assessment using measures “such as accuracy, precision, recall, F1 score, and ROC AUC”. This research leverages sophisticated machine learning techniques and tackles the specific challenges associated with imbalanced data. The study aims to significantly enhance the detection of fraudulent transactions while reducing false positives. The ultimate goal is to boost the security of financial systems, thus providing better protection against fraud, and to improve trust and reliability in credit card transactions.54 0Item Restricted Utilizing Data Analytics for Fraud Detection and Prevention in Online Banking Systems of Saudi Arabia(University of Portsmouth, 2024-09) Almotairy, Yazeed; Jiacheng, TanThis thesis addresses the critical issues of online banking and online banking fraud in Saudi Arabia. The thesis focusses on the older methodologies of the online banking systems in Saudi Arabia. The frauds are discussed in detail that are occurring in the online banking systems and are causing inconvenience to the users and account holders of the online banks and applications. In this thesis, online banking frauds are discussed thoroughly, and the traditional fraud detection methods are elaborated as well. The vulnerabilities in the current systems are explored. It discusses how the older systems are not performing well and why the new system encompasses the power of data analytics and machine learning. The methods proposed use a set of data analytics and machine learning algorithms and techniques to detect fraud or any fraudulent activity that a scammer or fraudster may perform. The results of this study explain how the proposed system can outperform the traditional methodologies being used in Saudi Arabian online banking systems. The proposed system can also enhance the user experience. The possible privacy and ethical concerns are also discussed. In the end, it is also discussed what the future prospects are for the researchers who are looking to enhance this research or want to work in the field of data analytics and machine learning to improve the security of the security of online banking applications. In conclusion, this thesis not only contributes to the body of knowledge on online banking frauds in Saudi Arabia and their detection but also features future research topics for new researchers.45 0