Awesome

OCAN: One-Class Adversarial Nets for Fraud Detection

In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data.

Running Environment

The main packages you need to install are listed as follow

1. python 2.7 
2. tensorflow 1.3.0

DateSet

For experiments, we evaluate OCAN on two real-world datasets: wiki and credit-card which have been attached in folder data/.

Model Evaluation

The command line for OCAN goes as follow

    python oc_gan.py $1 $2

where $1 refers to different datasets with wiki 1, credit-card(encoding) 2 and credit-card(raw) 3; $2 denotes whether some metrics, such as fm_loss and f1 in training process, are provided, with non-display 0 and display 1.

   e.g. python oc_gan.py 1 0 

The above command line shows the performance of OCAN on wiki without displaying metrics in the training process.