Bisrat Derebssa (PhD)Melat Kebede2024-07-312024-07-312024-06https://etd.aau.edu.et/handle/123456789/3311Mobile Money, a revolutionary technology, enables individuals to manage their bank accounts entirely via their mobile devices, allowing for transactions like bill payments with unmatched ease and efficiency.This innovation has significantly reshaped financial landscapes, particularly in developing countries with limited access to traditional banking, by promoting financial inclusion and driving economic opportunity. However, the rapid growth of mobile money services has introduced significant challenges, such as fraud, where unauthorized individuals manipulate the system through various scams, creating serious risks that lead to financial losses and undermining trust in the system. We propose a fraud detection model that integrates deep learning techniques to identify fraudulent transactions and adapt to the dynamic behaviors of fraudsters in mobile money transactions. Given the private nature of financial data, we utilized a synthetic dataset generated using the Pay Sim simulator, which is based on a company in Africa. We evaluated three deep learning architectures, namely Restricted Boltzmann Machine (RBM), Probabilistic Neural Network (PNN), and Multi-Layer Perceptron (MLP) for fraud detection, emphasizing feature engineering and class distribution. The MLP achieved 95.70% accuracy, outperforming the RBM (89.91%) and PNN (73.36%) across various class ratios and on both the original and feature-engineered datasets. Among various techniques for anomaly detection, the Auto-Encoder consistently outperformed others, such as the Isolation Forest and Local Outlier Factor, achieving an accuracy of 82.85%. Our hybrid model employed a feature augmentation approach, integrating prediction scores from an Autoencoder model as additional features. These scores were then fed into the Multi-layer Perceptron (MLP) model along with the original dataset. This hybrid approach achieved 96.56% accuracy, 97.62% precision, 84.16% recall, and a 90.39% F1-score, outperforming the standalone MLP.The Hybrid model achieved an accuracy of 73.33% on unseen dataset, showing a 3.9% increase over the MLP model’s 69.41% accuracy, and demonstrating its enhanced ability to capture and adapt to evolving fraud patterns.This study finds that the hybrid model’s enhanced performance highlights the significance of anomaly detection and feature engineering in improving fraud detection.en-USMLPAutoencoderMobile Money Transfer (MMT)Mobile Money FraudDeep Learning ModelsThesis