Predicting Fraud in Mobile Money Transactions using Machine Learning: The Effects of Sampling Techniques on the Imbalanced Dataset

Francis Effirim Botchey, Zhen Qin, Kwesi Hughes-Lartey, Ernest Kwame Ampomah


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.

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