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

SAFE: A Neural Survival Analysis Model for Fraud Early Detection