<p>This paper examines an in-depth ablation study of the Gated Recurrent Unit (GRU) architectures to determine the best model that can be used to detect anomalies in real transaction data. This study proposes to develop eight different variants of GRU, including a basic three-layer GRU standard and more sophisticated ones, including Bidirectional layers, Layer Normalization, and Attention models. The GRU architectures were trained to predict the anomaly score in financial transaction. Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics were used to determine training and testing errors. A dataset with 12043 financial transactions is used to evaluate the proposed GRU architectures. Furthermore, our research determines training time and overfitting gaps for all eight GRU models. Results show that the “GRU + Bidirectional + Layer Normalization” architecture gave the best results with the lowest Test MAE of 0.11135, which is lower than the Baseline Standard GRU with a Test MAE of 0.11173. Other prominent GRU architectures were “GRU + MultiHead Attention” and GRU + Layer Normalization, which showed slight improvements in MAE values. This study found that the more complicated structures, like the “Advanced GRU (Full) and the GRU + Bidirectional + Attention, showed surprisingly lower performance than the baseline GRU architecture. Cross-validation results demonstrated robust performance achieving AUC-PR values approaching 0.99 and a F1-score of 0.9755, confirmed the reliable anomaly detection in bank transactions. The findings of this study indicate that it is not necessarily the case that the complexity of the architecture correlates with better anomaly detection potential. The findings emphasize the importance of architectural decisions on the effectiveness and efficiency of models in time-series anomaly detectors.</p>