Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.

本文提出了一种基于生存分析的欺诈早期检测模型 (SAFE)，该模型采用递归神经网络 (RNN) 处理用户活动序列，并直接在每个时间戳上输出危险值，然后，从危险值衍生出生存概率，以实现一致的预测。实验结果表明 SAFE 在两个真实世界数据集上优于其他技术。