Mobile Money Services (MMS), enabled by the wide adoption of mobile phones, offered an opportunity for financial inclusion for the unbanked in developing nations. Meanwhile, the risks of cybercrime are increasing, becoming more widespread, and worsening. This is being aggravated by the inadequate security practises of both service providers and the potential customers' underlying criminal intent to undermine the system for financial gain. Predicting potential mobile money cyber threats will afford the opportunity to implement countermeasures before cybercriminals explore this opportunity to impact mobile money assets or perpetrate financial cybercrime. However, traditional security techniques are too broad to address these emerging threats to Mobile Financial Services (MFS). Furthermore, the existing body of knowledge is not adequate for predicting threats associated with the mobile money ecosystem. Thus, there is a need for an effective analytical model based on intelligent software defence mechanisms to detect and prevent these cyber threats. In this study, a dataset was collected via interview with the mobile money practitioners, and a Synthetic Minority Oversampling Technique (SMOTE) was applied to handle the class imbalance problem. A predictive model to detect and prevent suspicious customers with cyber threat potential during the onboarding process for MMS in developing nations using a Machine Learning (ML) technique was developed and evaluated. To test the proposed model's effectiveness in detecting and classifying fraudulent MMS applicant intent, it was trained with various configurations, such as binary or multiclass, with or without the inclusion of SMOTE. Python programming language was employed for the simulation and evaluation of the proposed model. The results showed that ML algorithms are effective for modelling and automating the prediction of cyber threats on MMS. In addition, it proved that the logistic regression classifier with the SMOTE application provided the best classification performance among the various configurations of logistic regression experiments performed. This classification model will be suitable for secure MMS, which serves as a key deciding factor in the adoption and acceptance of mobile money as a cash substitute, especially among the unbanked population.

In this paper, an investigation was made to evaluate the effectiveness of the different classifiers suitable to predict the probability of a cyber-threat or fraudulent intent applicant during the Mobile Money Service on-boarding or service activation process, with the goal of determining the best machine learning model for the predictive model solution. Experimental work was carried out by formulating cyber threat predictive models using six supervised machine learning algorithms: Logistic regression(LR), Naïve Bayes, Shallow Neural Network (SNN), Deep Neural Network (DNN), Classification and Regression Trees (CART) and Random Forest (RF) of different configurations. Each model was simulated with both Synthetic Minority Operation Techniques (SMOTE) and without SMOTE (No-SMOTE) on 25,000 records of mobile money applicants. Twenty-four ( 24) different configurations of the formulated predictive models were simulated and evaluated using the Python programming language. Simulation results of the predictive models proved that the Random Forest model multiclass configurations with the SMOTE dataset outperformed all other configurations. The results also showed that the multiclass experiments with SMOTE had better performance than the binary configurations with NO-SMOTE in the predictive models. The study concluded that using the Random Forest-based predictive machine learning model will increase the security level of the Mobile Money solution by detecting and preventing anomalous customer registrations during the unbanked onboarding process.Povzetek: Napovedovanje kibernetskih groženj mobilnega denarja z uporabo algoritmov strojnega učenja. 22 RF Binary-SMOTE Random Forest(RF) with Binary feature configuration and with SMOTE application to Dataset 23 RF Multiclass-No SMOTE Random Forest(RF) with Multiclass feature configuration and No-SMOTE application to Dataset 24 RF Multiclass-SMOTE Random Forest(RF) with Multiclass feature configuration and with SMOTE application to Dataset