SAFE: A Neural Survival Analysis Model for Fraud Early Detection

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.Comment: To appear in AAAI-201

An Epistemological and Pattern Analysis of Empirical Data that Influences Emergency Loan Need Among Graduate Students

This analysis studies closely education affordability through the epistemology of emergency loan need that signals economic challenges on the horizon for domestic and international students seeking a post graduate credential at any cost. Prior studies have been very helpful; however, to the best of our knowledge there is not a comprehensive study that has investigated the comparison of small vs. large emergency student loans taken out by graduate students. Also, to the best of our knowledge and to date there are no studies that have investigated the patterns and relationships among ethnicity, gender, marital status, degree type, and college awarded for both small vs. large emergency loans. To fill the gaps in the literature, we conducted our research by collecting datasets from 335 graduate students enrolled in a large public university located in North America. Our data analysis provides strong indicators and evidence that both small and large emergency loan needs exist in a diverse spectrum of colleges, degree types, ethnicities, genders, ages, and marital statuses. Also, the regression analysis indicates that there is not a significant relationship between GPA and emergency loan needs for both small and large loans. We also, used data mining technique to investigate patters and relationships among ethnicity, gender, marital status, degree type, and college awarded for both small vs. large emergency loans. Our study contains vast research and managerial implications for both academia and top managements

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian

The extraction of clusters from a dataset which includes multiple clusters and a significant background component is a non-trivial task of practical importance. In image analysis this manifests for example in anomaly detection and target detection. The traditional spectral clustering algorithm, which relies on the leading $K$ eigenvectors to detect $K$ clusters, fails in such cases. In this paper we propose the {\it spectral embedding norm} which sums the squared values of the first $I$ normalized eigenvectors, where $I$ can be significantly larger than $K$. We prove that this quantity can be used to separate clusters from the background in unbalanced settings, including extreme cases such as outlier detection. The performance of the algorithm is not sensitive to the choice of $I$, and we demonstrate its application on synthetic and real-world remote sensing and neuroimaging datasets