With the proliferation of various online and mobile payment systems, credit
card fraud has emerged as a significant threat to financial security. This
study focuses on innovative applications of the latest Transformer models for
more robust and precise fraud detection. To ensure the reliability of the data,
we meticulously processed the data sources, balancing the dataset to address
the issue of data sparsity significantly. We also selected highly correlated
vectors to strengthen the training process.To guarantee the reliability and
practicality of the new Transformer model, we conducted performance comparisons
with several widely adopted models, including Support Vector Machine (SVM),
Random Forest, Neural Network, and Logistic Regression. We rigorously compared
these models using metrics such as Precision, Recall, and F1 Score. Through
these detailed analyses and comparisons, we present to the readers a highly
efficient and powerful anti-fraud mechanism with promising prospects. The
results demonstrate that the Transformer model not only excels in traditional
applications but also shows great potential in niche areas like fraud
detection, offering a substantial advancement in the field.

通过使用最新的 Transformer 模型进行数据处理和高度相关向量选择，本研究致力于创新应用于更可靠和精确的欺诈检测，通过与多个广泛采用的模型进行性能比较，包括支持向量机（SVM），随机森林，神经网络和逻辑回归等，比较指标如 Precision，Recall 和 F1 Score，结果表明 Transformer 模型在传统应用中表现出色，并在欺诈检测等领域展示了巨大潜力，为该领域带来实质性进展。