For the highly imbalanced credit card fraud detection problem, most existing methods either use data augmentation methods or conventional machine learning models, while neural network-based anomaly detection approaches are lacking. Furthermore, few studies have employed AI interpretability tools to investigate the feature importance of transaction data, which is crucial for the black-box fraud detection module. Considering these two points together, we propose a novel anomaly detection framework for credit card fraud detection as well as a model-explaining module responsible for prediction explanations. The fraud detection model is composed of two deep neural networks, which are trained in an unsupervised and adversarial manner. Precisely, the generator is an AutoEncoder aiming to reconstruct genuine transaction data, while the discriminator is a fully-connected network for fraud detection. The explanation module has three white-box explainers in charge of interpretations of the AutoEncoder, discriminator, and the whole detection model, respectively. Experimental results show the state-of-the-art performances of our fraud detection model on the benchmark dataset compared with baselines. In addition, prediction analyses by three explainers are presented, offering a clear perspective on how each feature of an instance of interest contributes to the final model output.
翻译:对于高度不平衡的信用卡欺诈检测问题,大多数现有方法要么使用数据增强方法,要么使用常规机器学习模式,而神经网络的异常检测方法则缺乏。此外,很少有研究采用AI解释工具来调查交易数据的特殊重要性,这对黑箱欺诈检测模块至关重要。考虑到这两点,我们提出一个新的信用卡欺诈检测异常检测框架以及一个负责预测解释的示范解释模块。欺诈检测模式由两个深层神经网络组成,这些网络以不受监管和对抗的方式培训。准确地说,生成者是一个旨在重建真实交易数据的自动Encoder,而歧视者是一个完全连接的欺诈检测网络。解释模块有3个白箱解释器分别负责对AutoEncoder、歧视者和整个检测模型进行解释。实验结果显示我们在基准数据集与基线相比的欺诈检测模型上的最新表现。此外,3个解释者还提出了预测分析,清晰地展示了每个感兴趣的特性如何促进最终模型。