Fraud detection in mobile money transactions using differentially private machine learning techniques

Abstract

Mobile money has become a vital financial tool in regions with limited banking infrastructure, fostering financial inclusion for underserved populations. However, its rapid growth has led to rising fraud threats such as refund scams, account takeovers, and identity misuse. Machine learning (ML) models show promise in detecting fraud through transaction patterns, but they rely heavily on sensitive personal data, raising concerns about privacy, security, and regulatory compliance. These risks may reduce user trust and hinder adoption. This thesis explores the use of privacy-preserving ML, specifically differential privacy to enable effective fraud detection while safeguarding user data. This study adapts privacy preserving ML models by integrating differential privacy into supervised algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Trees, and Random Forest. A labeled synthetic Mobile money transaction dataset is used to train these models, leveraging the IBM Diffpriv library to enforce differential privacy constraints. Model performance was further assessed across varying privacy levels using the differential privacy parameter e to evaluate the privacy utility trade off. The results showed that Logistic Regression experienced substantial degradation under strict privacy € = 1, with accuracy dropping to 0.496 and recall to 0.047, indicating high sensitivity to privacy noise. Meanwhile, Gaussian Naive Bayes, Decision Trees, and Random Forest remained robust, maintaining high accuracy and recall even at lower epsilon values. Across the full range of privacy budgets, Random Forest and Gaussian Naive Bayes demonstrated the most stable performance, highlighting their suitability for privacy preserving fraud detection. These results indicate that differentially private machine learning models can effectively detect frandulent transactions while preserving user privacy. The findings contribute to the development of secure, ethical and privacy sensitive fraud detection systems for mobile financial services.