An enhanced machine learning Genetic Algorithm for detecting mobile money fraud

Abstract

The increased level of financial transactions before and specifically after the influx of COVID-19 has heightened the activities of fraudsters in the mobile money sector. This calls for the development of robust systems that would effectively detect and if possible prevent these unscrupulous occurrences to a large extent. The features of mobile money transactions dataset are highly unstructured, and therefore need to be streamlined using a powerful supervised machine learning tool. Genetic Algorithm has been proposed as an effective feature selection method in this paper. Selected machine learning algorithms were used to further build the models for the fraud detection system. These ML techniques include Artificial Neural Networks, Random Forest, Naïve Bayes, Decision Trees and Logistic Regression. In order to test the efficacy of the classifiers, the performance of the models were validated using mobile money dataset obtained from kaggle database. The findings from this work have clearly proven that the proposed algorithm used for the feature selection exhibits greater efficiency than the existing machine learning techniques.