Mobile Banking and Mobile Money Banking Fraud Detection Using Machine Learning on Banks in Ethiopia
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Date
2024-02
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Addis Ababa University
Abstract
Fraud is a criminal act with significant societal impacts, particularly in the financial sector. As digitalization expands rapidly in Ethiopia, the financial industry has suffered substantial losses, with an estimated 1.8 billion birr lost to fraud over the past four years. This study aims to explore and evaluate the effectiveness of SVM machine learning technique for detecting fraud in mobile banking and mobile money services within the Ethiopian banking sector. This research employs a quantitative experimental approach to investigate fraud detection in mobile banking and mobile money services using machine-learning models, particularly Support Vector Machines (SVM).
A comprehensive literature review reveals that while mobile banking and mobile money have become essential in East African countries, including Ethiopia, there is a significant gap in research addressing fraud detection in this context. As the digital financial landscape evolves, the threat of fraud is becoming increasingly severe, posing a substantial challenge to the region's financial stability. This study aims to bridge this research gap by exploring and evaluating the effectiveness of machine learning (ML) techniques for detecting fraud in mobile banking and mobile money services. Utilizing the CRISP-DM framework for data mining, the study apply SVM supervised ML techniques to transaction data from these platforms. To address the class imbalance inherent in the data, under
sampling techniques were employed, with the dataset split into training (80%) and testing (20%) sets after the necessary data cleaned and preparation based on the framework selected has been carried out. In this study, the data taken for analysis is the transaction data of mobile banking and mobile money this is because the fraudulent activities on one of the channel may come to the other as the services are having many similarities in nature.
The performance of a Support Vector Machine (SVM) model was assessed using metrics such as Precision, Recall, and Confusion Matrix. Initial findings indicate that the model struggles with the class imbalance, which affects its overall effectiveness but still identify 51% of the fraudulent transactions. Despite these challenges, this study provides valuable insights into the application of machine learning for fraud detection in mobile banking and mobile money services within East African region where such practices are still emerging.
This research contributes to the limited body of knowledge on fraud detection in the rapidly expanding digital financial services sector in East Africa, offering a foundation for future studies and practical applications in the financial section in the region and beyond.
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Keywords
Fraud Detection, Mobile Banking Fraud Detection, Machine Learning, Mobile Money Banking, Mobile Banking