Abstract
Financial fraud poses a significant and escalating threat to global economies, leading to substantial monetary losses and eroding public trust. The early detection of fraudulent activities is paramount, yet it is severely challenged by the inherently imbalanced nature of transactional datasets, where fraudulent instances are exceedingly rare, and the sequential dependencies within transaction streams are complex. Traditional fraud detection methods often struggle with these characteristics, exhibiting limitations in capturing long-range patterns and effectively handling class imbalance. This research proposes a novel approach leveraging Transformer networks, renowned for their prowess in processing sequential data and capturing intricate dependencies through self-attention mechanisms, for the early detection of financial fraud. We adapt the Transformer architecture to model sequences of financial transactions, integrating advanced techniques to mitigate the impact of extreme class imbalance. Experimental evaluation on a representative transactional dataset demonstrates that the proposed Transformer-based model significantly outperforms traditional machine learning and recurrent neural network baselines across critical metrics such as Area Under the Precision-Recall Curve (AUC-PR) and F1-score for the minority class. The findings underscore the potential of Transformer networks to enhance the accuracy and timeliness of fraud detection, offering a robust solution for financial institutions.
