SAFE: A Neural Survival Analysis Model for Fraud Early Detection

TL;DR

SAFE introduces a survival analysis neural network that predicts the likelihood of user fraud over time, ensuring consistent early detection and outperforming existing methods on real datasets.

Contribution

The paper presents a novel survival analysis-based neural model for fraud detection that guarantees monotonic survival probabilities and effectively handles censored data.

Findings

Outperforms existing fraud detection models on real datasets.

Ensures monotonic decrease of survival probabilities over time.

Effectively handles censored data in training.

Abstract

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.