A Predictive Cyber Threat Model for Mobile Money Services

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Abstract
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.

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