@article{article_1634530, title={Advanced Mobile Money Fraud Detection Using CNN-BiLSTM and Optimized SGD with Momentum}, journal={AJIT-e: Academic Journal of Information Technology}, volume={16}, pages={207–231}, year={2025}, DOI={10.5824/ajite.2025.03.002.x}, author={Yussif, Niamatu and Takyi, Kate and Owusuaa Mensah Gyening, Rose-mary and Israel Boadu-acheampong, Samuelson}, keywords={Fraud Detection, Machine Learning, Neural Networks, Stochastic Gradient Descent}, 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.}, number={3}, publisher={Akademik Bilişim Araştırmaları Derneği}