Fraud detection is a critical task across various domains, requiring accurate identification of fraudulent activities within vast arrays of transactional data. The significant challenges in effectively detecting fraud stem from the inherent class imbalance between normal and fraudulent instances. To address this issue, we propose a novel approach that combines autoencoder-based noise factor encoding (NFE) with the synthetic minority oversampling technique (SMOTE). Our study evaluates the efficacy of this approach using three datasets with severe class imbalance. We compare three autoencoder variants—autoencoder (AE), variational autoencoder (VAE), and contractive autoencoder (CAE)—enhanced by the NFE technique. This technique involves training autoencoder models on real fraud data with an added noise factor during the encoding process, followed by combining this altered data with genuine fraud data. Subsequently, SMOTE is employed for oversampling. Through extensive experimentation, we assess various evaluation metrics. Our results demonstrate the superiority of the autoencoder-based NFE approach over the use of traditional oversampling methods like SMOTE alone. Specifically, the AE–NFE method outperforms other techniques in most cases, although the VAE–NFE and CAE–NFE methods also exhibit promising results in specific scenarios. This study highlights the effectiveness of leveraging autoencoder-based NFE and SMOTE for fraud detection. By addressing class imbalance and enhancing the performance of fraud detection models, our approach enables more accurate identification and prevention of fraudulent activities in real-world applications.

欺诈检测是跨各个领域的一项关键任务，需要准确识别大量交易数据中的欺诈活动。有效检测欺诈的重大挑战源于正常实例和欺诈实例之间固有的类别不平衡。为了解决这个问题，我们提出了一种新颖的方法，它将基于自动编码器的噪声因子编码（NFE）与合成少数过采样技术（SMOTE）相结合。我们的研究使用三个类别严重不平衡的数据集评估了这种方法的有效性。我们比较了由 NFE 技术增强的三种自动编码器变体：自动编码器 (AE)、变分自动编码器 (VAE) 和收缩自动编码器 (CAE)。该技术涉及在编码过程中添加噪声因子的真实欺诈数据上训练自动编码器模型，然后将这些更改后的数据与真实欺诈数据相结合。随后，采用SMOTE进行过采样。通过广泛的实验，我们评估了各种评估指标。我们的结果证明了基于自动编码器的 NFE 方法相对于单独使用 SMOTE 等传统过采样方法的优越性。具体来说，尽管 VAE-NFE 和 CAE-NFE 方法在特定场景中也表现出有希望的结果，但在大多数情况下，AE-NFE 方法优于其他技术。这项研究强调了利用基于自动编码器的 NFE 和 SMOTE 进行欺诈检测的有效性。通过解决类别不平衡问题并增强欺诈检测模型的性能，我们的方法可以更准确地识别和预防现实应用中的欺诈活动。