Advanced Mobile Money Fraud Detection Using CNN-BiLSTM and Optimized SGD with Momentum

Authors : Niamatu Yussif, Kate Takyi, Rose-mary Owusuaa Mensah Gyening, Samuelson Israel Boadu-acheampong

Pages : 207-231

Doi:10.5824/ajite.2025.03.002.x

View : 85 | Download : 126

Publication Date : 2025-08-31

Article Type : Research Paper

Abstract :The accelerated adoption of mobile money systems has significantly increased fraudulent activity, compromising their security and trustworthiness. This research presents an enhanced method for detecting mobile money fraud by modifying a CNN-BiLSTM model with momentum using Stochastic Gradient Descent (SGD). We computed salient features from transaction data using a pre-processed hybrid CNN-BiLSTM model and trained the model to identify trends in the data that included geographical and temporal aspects. The model performed remarkably using industry-standard testing approaches: an F1 score of0.9928, precision of 0.9927, accuracy of 0.9928, and recall of 0.9929. The proposed model can identify dishonesty and has a low false positive rate. According to the study, the model improves feature selection and incorporates various optimization techniques, making it more flexible and suitable for different mobile money systems.
Keywords : Sahtecilik Tespiti, Makine Öğrenmesi, Sinir Ağları, Stokastik Gradyan İnişi

ORIGINAL ARTICLE URL