Abstract
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 include mobile money transactions are, however, skewed towards the negative class. Machine learning models built with such datasets are unreliable as the prediction algorithms will be biased towards the negative class. 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.
Highlights
The use of mobile devices have become a rudimentary part of our daily lives
For a model to be considered adequate for classification and used subsequently for prediction, one of the key indicators is the evaluation of the True Positive Rate (TPR), TNR, false positive rate (FPR), and fraudulent transactions classified as legitimate ones (FNR)
TNR, FPR, and FNR of the models used in our experiments, all the seven(7) models produced TPR and TNR values that exceed 70% in their respective domains
Summary
The use of mobile devices have become a rudimentary part of our daily lives. The way we conduct our daily activities have become heavily dependent on mobile devices. One significant aspect of our interactions with mobile devices that cannot be overemphasized is the way we conduct financial transactions. In most part of the developing world, access to formal financial services becomes virtually impossible as the infrastructure and services needed for formal financial inclusion are nonexistent. Where these financial infrastructure exist, often, customers have to travel long distances in order to access these services culminating in additional cost to the already impoverished individual. The implication of financial exclusion is that individuals with no access to conventional financial services tend to be poor and this is vividly evident in most developing countries
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