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.

Internet of Things (IoT) presents opportunities for designing new technologies for organizations. Many organizations are beginning to accept these technologies for their daily work, where employees can be connected, both on the organization's premises and the "outside", for business continuity. However, organizations continue to experience data breach incidents. Even though there is a plethora of researches in Information Security, there "seems" to be little or lack of interest from the research community, when it comes to human factors and its relationship to data breach incidents. The focus is usually on the technological component of Information Technology systems. Regardless of any technological solutions introduced, human factors continue to be an area that lacks the required attention. Making the assumption that people will follow expected secure behavioral patterns and therefore system security expectations will be satisfied, may not necessarily be true. Security is not something that can simply be purchased; human factors will always prove to be an important space to explore. Hence, human factors are without a doubt a critical point in Information Security. In this study, we propose an Organizational Information Security Framework For Human Factors applicable to the Internet of Things, which includes countermeasures that can help prevent or reduce data breach incidents as a result of human factors. Using linear regression on data breach incidents reported in the United States of America from 2009 to 2017, the study validates human factors as a weak-point in information security that can be extended to Internet of Things by predicting the relationship between human factors and data breach incidents, and the strength of these relationships. Our results show that five breach incidents out of the seven typified human factors to statistically and significantly predict data breach incidents. Furthermore, the results also show a positive correlation between human factors and these data breach incidents.

Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which includes mobile money transactions are however, skewed towards the legitimate transactions. Machine learning models built with such skewed datasets are unreliable as the prediction algorithms will be biased towards the legitimate transactions. We investigate the performance of different sampling and weighting techniques such as Adaptive Synthetic Sampling (ADASYN) and Synthetic Minority Oversampling Technique (SMOTE). We select Logistic Regression for the experiments due to its simplicity and relatively low computational needs. The performance is evaluated with different metrics. Manually tuning the weights of the classes achieved the best results in our experiments.Povzetek: Opisana je metoda za detekcijo prevar v mobilnih transakcijah s pomočjo strojnega učenja na neuravnoteženih podatkih.