Title: Mobile Money Fraud Detection System Using Machine Learning
Authors: Gabriel F. Manga, Goodluck A. Nyoni, Athuman R Mganga, Boniface Kadege, Alfred Lukenza, Saul Vyagusa, Nuru Gerson, Neema Mathias, Vincent Bob.
Volume: 10
Issue: 6
Pages: 318-321
Publication Date: 2026/06/28
Abstract:
: The rapid growth of mobile money services has transformed financial transactions, particularly in developing countries, by providing secure, fast, and accessible digital payment solutions. Despite these benefits, the increasing adoption of mobile money platforms has also attracted various forms of fraudulent activities, including identity theft, account takeover, phishing attacks, and unauthorized transactions. Traditional fraud detection methods often rely on predefined rules, making them ineffective against evolving fraud patterns. This review paper examines the application of machine learning techniques in mobile money fraud detection systems. The study explores existing approaches, algorithms, datasets, and challenges associated with fraud detection. The review highlights how machine learning models can analyze transaction behavior, identify anomalies, and improve fraud detection accuracy in real time. Furthermore, the paper identifies research gaps and proposes directions for future improvements in developing intelligent and adaptive fraud detection systems.