Fraud Detection Combating Mobile Money Fraud in SMS Messages Using Machine Learning
Abstract
This paper presents a machine learning-based fraud detection model to combat the SMS based mobile money fraud. The systems use an XGBoost classifier to process SMS message content real-time and boast a high level of accuracy with predicting malicious intent. Built using Flask framework, the system allows administrators to monitor in real-time all reported alarms for specific transactions regarding fraud and keep statistical records. Future research includes improving the interpretability of models and making them both more scalable as well as robust to adversarial attacks for better fraud detection in mobile money services.
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