Fraud Detection in Financial Transactions Using Deep Neural Networks

Fraud or Fake financial transactions seriously impact digital payment systems, necessitating more advanced detection mechanisms to mitigate the associated risks. Fraud trends that are always changing have made the traditional methods used to identify fraud cases obsolete, such as rule-based fraud detection and machine learning models. Recent studies have shown that Graph Neural Networks (GNNs) can better capture the relationship between financial transactions, while transformers are effective at recognizing sequential fraud patterns. Yet, the existing models that incorporate both do not perform well in this manner. To fill this gap in existing research, we have created a new model for detecting fraudulent transactions, the Hybrid GNN-Transformer Fraud Detection Model. This uses graphbased learning along with deep sequential feature extraction to better distinguish frauds from genuine transactions. The hybrid model had better performance compared to single models such as autoencoders, GNNs, and LSTMs, getting an accuracy of $99 \%$, as well as a precision of.99 and a recall of 1.00 when it comes to detecting fraudulent transactions. Comparisons show that GNNs and LSTMs still, when combined with transformers, there is an improved ability in the identification ofare able to capture key transaction interdependencies on their own. Still, when combined with transformers, they have an improved ability to identify complicated fraud activities.