Applying the Paysim synthetic dataset, this project aims to develop and evaluate ML models for mobile money fraud detection. We aim to evaluate how well sophisticated approaches, especially ANN and hybrid architectures, can detect fraudulent actions. There are multiple essential stages to the approach. First, we gather the Paysim dataset, which includes information like the kinds of transactions, their prices, account balances, and any signs of fraud. Exploratory Data Analysis (EDA) gives insights into transaction patterns after data pretreatment cleans and standardizes the dataset. It is feasible to build or train several models using the best optimization strategy after data is partitioned into testing and training sets. Hybrid designs (including RNNs, LSTMs, and GRUs) and artificial neural networks comprise these models. Examining the model's accuracy or predictive capacities allows us to gauge its performance. Among the models that were constructed, the ANN model stands out to be the top performer due to its exceptional accuracy and great predictive abilities. Its ability to efficiently capture complicated fraud patterns is due to its sequential architecture, which is complemented by many hidden layers and optimization approaches. The ANN's performance highlights the significance of using sophisticated deep learning architectures to fraud detection jobs. This study provides useful insights for financial organizations looking to improve their fraud detection mechanisms by highlighting the necessity of employing realistic synthetic datasets to system development and testing.