Machine learning detects Ghana mobile money scam messages accurately
Machine Learning-Based Detection of Mobile Money Scan Messages in Ghana: A Comparative Evaluation of Naive Bayes, Support Vector Machine, and Randon Forest Classifiers
Machine Learning
Summary
Mobile money services in Ghana are very popular but face many scam messages trying to steal personal information through SMS. The authors created a method that uses machine learning to tell scam messages apart from real ones by looking at the words used. They tested three common machine learning models and found two of them could perfectly separate scams from legitimate messages on their data. This approach could help block scam messages on phones or networks, especially in areas with limited resources.
What this means in practice
- •For mobile network operators: Automatically filter scam mobile money messages on their networks to reduce fraud exposure for subscribers in Ghana.
- •For smartphone software developers: Integrate on-device scam message detection to warn users about fraudulent mobile money texts in low-resource settings.
Tested on one dataset.
Authors
Ibrahim Salifu, Muhaisin Tiyumba Nantogmah
Abstract
Mobile money (MoMo) is Ghana's preferred retail payment channel, recording GHC518.4 billion in transactions in December 2025 and over 26.7 million active accounts (Graphic Online, 2026). This growth has coincided with increased social-engineering fraud over Short Message Service (SMS) and Unstructured Supplementary Service Data (USSD), where fraudsters impersonate network operators, agents, relatives, or regulators to obtain personal identification numbers (PINs), identity data, or funds. We present a supervised machine-learning pipeline for classifying scam and legitimate Ghanaian MoMo SMS messages using a labelled corpus of 220 messages across 18 scam and legitimate subcategories. Domain-specific normalisation of currency amounts, phone numbers, and USSD codes preceded TF-IDF vectorisation of unigrams and bigrams. Using stratified five-fold cross-validation and a 20% held-out test set, we trained and evaluated Multinomial Naive Bayes, linear support vector machine (SVM), and Random Forest classifiers using accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC-ROC). Held-out accuracy, F1, and AUC were 1.00 for Naive Bayes and linear SVM; cross-validated accuracy averaged 96.6% (+/-3.3), 94.9% (+/-4.6), and 92.0% (+/-4.2). Discriminative n-grams reflected imperative urgency ("send," "claim," "please send") and PIN solicitation ("PIN," "pincode"); legitimate messages featured confirmatory and cautionary language ("balance," "successful," "do not share"). Findings are contextualised in Sub-Saharan African SMS fraud detection, small template-based corpus ceiling effects, and a pathway toward larger naturalistic multilingual corpora. Interpretable classical classifiers achieve high accuracy on structured MoMo scam text and suit carrier- or handset-level filtering in low-resource, on-device settings.