Fraud Detection in Financial Transactions: A Unified Deep Learning Approach

Abstract

Financial fraud has emerged as a major challenge in today's digital economy, with an increasing number of fraudulent activities targeting online financial systems. This study proposes a unified deep learning approach for detecting fraudulent financial transactions using advanced neural network architectures. Traditional fraud detection methods rely heavily on rule-based or shallow learning algorithms, which often fail to detect novel fraud patterns. In contrast, this research introduces a hybrid framework incorporating convolutional neural networks (CNNs), gated recurrent units (GRUs), and attention mechanisms to capture both spatial and temporal dependencies within transaction sequences. We use a benchmark dataset of anonymized financial transactions, apply comprehensive preprocessing steps including normalization, class balancing, and feature engineering, and evaluate model performance using multiple metrics: RMSE, MAPE, and R^2. Experimental results show that the unified model outperforms conventional machine learning techniques and individual deep learning models in terms of accuracy and robustness. Furthermore, visualizations such as confusion matrices, ROC curves, and prediction plots are used to interpret model effectiveness. This work demonstrates that a unified deep learning strategy not only enhances detection performance but also provides a scalable solution for real-world financial institutions. Our findings highlight the necessity of integrating multiple deep learning architectures to address complex fraud scenarios effectively. Future work aims to extend this model to multimodal data sources such as social behavior and geolocation for enhanced fraud profiling.