As the financial industry becomes more interconnected and reliant on digital systems, fraud detection systems must evolve to meet growing threats. Cloud-enabled Transformer models present a transformative opportunity to address these challenges. By leveraging the scalability, flexibility, and advanced AI capabilities of cloud platforms, companies can deploy fraud detection solutions that adapt to real-time data patterns and proactively respond to evolving threats. Using the Graph self-attention Transformer neural network module, we can directly excavate gang fraud features from the transaction network without constructing complicated feature engineering. Finally, the fraud prediction network is combined to optimize the topological pattern and the temporal transaction pattern to realize the high-precision detection of fraudulent transactions. The results of antifraud experiments on credit card transaction data show that the proposed model outperforms the 7 baseline models on all evaluation indicators: In the transaction fraud detection task, the average accuracy (AP) increased by 20% and the area under the ROC curve (AUC) increased by 2.7% on average compared with the benchmark graph attention neural network (GAT), which verified the effectiveness of the proposed model in the detection of credit card fraud transactions.

翻译：随着金融行业日益互联并依赖于数字系统，欺诈检测系统必须不断演进以应对日益增长的威胁。基于云的Transformer模型为解决这些挑战提供了变革性机遇。通过利用云平台的可扩展性、灵活性和先进的人工智能能力，企业可以部署能够适应实时数据模式并主动响应不断演变的威胁的欺诈检测解决方案。利用图自注意力Transformer神经网络模块，我们可以直接从交易网络中挖掘团伙欺诈特征，而无需构建复杂的特征工程。最终，结合欺诈预测网络来优化拓扑模式与时间交易模式，以实现对欺诈交易的高精度检测。在信用卡交易数据上的反欺诈实验结果表明，所提出的模型在所有评估指标上均优于7个基线模型：在交易欺诈检测任务中，与基准图注意力神经网络（GAT）相比，平均准确率（AP）提升了20%，受试者工作特征曲线下面积（AUC）平均提升了2.7%，验证了该模型在信用卡欺诈交易检测中的有效性。