Journal of Engineering Research and Reports

The rapid digitalisation of banking has increased exposure to financial cybercrime, while existing fraud-detection methods remain limited by single-modality data, class imbalance, concept drift, and poor explainability. This study developed and evaluated the Generative AI-Enhanced Multimodal Transformer Framework (GAMT-Fraud), an explainable artificial intelligence model that integrates transactional, behavioural, and network data for fraud detection. The framework combines a multimodal attention transformer, gradient-boosted learning, and variational autoencoder-based anomaly detection, while synthetic minority augmentation addresses data imbalance. Using a quantitative experimental design, the framework was trained and validated on the Institute of Electrical and Electronics Engineers Computational Intelligence Society (IEEE-CIS) and PaySim benchmark datasets through stratified data partitioning, five-fold cross-validation, and bootstrap significance testing. Performance was evaluated using precision, recall, F1-score, area under the ROC curve (AUC), Matthews correlation coefficient, and precision-recall area. Results showed that GAMT-Fraud consistently outperformed conventional machine-learning and deep-learning baselines across both datasets, achieving statistically significant improvements in fraud-detection performance. Shapley-value-based explainability further enhanced transparency and regulatory compliance by providing interpretable decision insights. The study demonstrates that integrating generative AI, sequential learning, and relational analysis within a unified framework can improve fraud-detection effectiveness. It contributes a scalable, explainable, and auditable fraud-intelligence architecture and provides a replicable foundation for future research in multimodal and adversarial financial fraud detection. The framework is presented as an experimental and auditable proof of concept rather than evidence of immediate real-world deployment.