Mobile Money Fraud Prediction—A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

• Published in: Information (Basel) Vol. 11; no. 8; p. 383
• Main Authors: Botchey, Francis Effirim, Qin, Zhen, Hughes-Lartey, Kwesi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Accuracy; Algorithms; Credit card fraud; Datasets; Decision analysis; Decision trees; Deep learning; Developing countries; Experiments; FinTech; fraud; Industrialized nations; LDCs; Machine learning; mobile money transaction; Neural networks; Prevention; Smartphones; Support vector machines; Viruses
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info11080383

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.