  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.
