We live in an era where routine transactions ranging from paying domestic bills to buying groceries are carried out using mobile financial services. However, the rapid growth and uptake of these services has led to amplified security and privacy risks, including SIM swap attacks, identity fraud, data theft, refund fraud, and unauthorized fees. Advances in machine learning (ML) show potential for detecting financial fraud in mobile money transactions, yet this requires access to large volumes of transaction data. Research on mobile money fraud has been hindered by data sensitivity and privacy concerns that restrict access to such datasets. In addition, real mobile money datasets are class-imbalanced, with far fewer frauds than legitimate transactions, biasing ML models against the minority class. This thesis presents a differentially private synthetic data generation…
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