Enkono, Fillemon S.Suresh, Nalina2020-06-302020-06-302020-06-30Enkono, F. S., & Suresh, N. (2020). Application of machine learning classification to detect fraudulent e‑wallet deposit notification SMSes. The African Journal of Information and Communication (AJIC), 25, 1-13. https://doi.org/10.23962/10539/291952077-7213 (online version)2077-7205 (print version)https://hdl.handle.net/10539/29195Fraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.enThis article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence: https://creativecommons.org/licenses/by/4.0m-banking, e-wallets, short message service messages (SMSes), deposit notification, fraud, ham SMSes, scam SMSes, detection, machine learning, classifiers, naïve Bayes (NB), support vector machine (SVM), classification accuracy (CA), feature extraction, feature selectionArticle