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

Least squares estimator for path-dependent McKean-Vlasov SDEs via discrete-time observations

In this paper, we are interested in least squares estimator for a class of path-dependent McKean-Vlasov stochastic differential equations (SDEs). More precisely, we investigate the consistency and asymptotic distribution of the least squares estimator for the unknown parameters involved by establishing an appropriate contrast function. Comparing to the existing results in the literature, the innovations of our paper lie in three aspects: (i) We adopt a tamed Euler-Maruyama algorithm to establish the contrast function under the monotone condition, under which the Euler-Maruyama scheme no longer works; (ii) We take the advantage of linear interpolation with respect to the discrete-timeobservations to approximate the functional solution; (iii) Our model is more applicable and practice as we are dealing with SDEs with irregular coefficients (e.g., H\"older continuous) and path-distribution dependent

Analysis in-Depth of the Factors that Impact the Development of E-Commerce in Underdeveloped Areas from the Perspective of Operating Process

The booming e-commerce has played an important role in the growth of regional economy and the e-commerce of underdeveloped regions has been developing rapidly as well. However, there is a large gap between underdeveloped and developed regions due to the limitation of economic foundation, geographical condition and other factors. The research of effects on the e-commerce development of underdeveloped regions played an important role in the promotion of e-commerce. Based on the perspective of operating process, starting from the indicator of information flow, capital flow and logistics, this article established the indicator system of effects on e-commerce in underdeveloped region. After collecting data by questionnaire survey, getting the right of each index by AHP and comparing the differences of important indicators between the en-commerce developments in the eastern and western important indicators, summarize the factors which affect the e-commerce development of underdeveloped regions, proposes to suggestions such as strengthen the guide of government functions and so on

Investor attention and carbon return: evidence from the EU-ETS

This paper firstly puts forward to employ investor attention obtained from Google trends to explain and forecast carbon futures return in the European Union-Emission Trading Scheme (EU-ETS). Our empirical results show that investor attention is a granger cause to changes in carbon return. Furthermore, investor attention generates both linear and non-linear effects on carbon return. The results demonstrate that investor attention shows excellent explanatory power on carbon return. Moreover, we conduct several out-of-sample forecasts to explore the predictive power of investor attention. The results indicate that incorporating investor attention indeed improve the accuracy of out-of-sample forecasts both in short and long horizons and can generate significant economic values. All results demonstrate that investor attention is a non-negligible pricing factor in carbon market

One-Class Adversarial Nets for Fraud Detection

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.Comment: Update Fig 2, add Fig 7, and add reference

Fully Conjugated Phthalocyanine Copper Metal-Organic Frameworks for Sodium-Iodine Batteries with Long-Time-Cycling Durability

Rechargeable sodium-iodine (Na-I-2) batteries are attracting growing attention for grid-scale energy storage due to their abundant resources, low cost, environmental friendliness, high theoretical capacity (211 mAh g(-1)), and excellent electrochemical reversibility. Nevertheless, the practical application of Na-I-2 batteries is severely hindered by their poor cycle stability owing to the serious dissolution of polyiodide in the electrolyte during charge/discharge processes. Herein, the atomic modulation of metal-bis(dihydroxy) species in a fully conjugated phthalocyanine copper metal-organic framework (MOF) for suppression of polyiodide dissolution toward long-time cycling Na-I-2 batteries is demonstrated. The Fe-2[(2,3,9,10,16,17,23,24-octahydroxy phthalocyaninato)Cu] MOF composited with I-2 (Fe-2-O-8-PcCu/I-2) serves as a cathode for a Na-I-2 battery exhibiting a stable specific capacity of 150 mAh g(-1) after 3200 cycles and outperforming the state-of-the-art cathodes for Na-I-2 batteries. Operando spectroelectrochemical and electrochemical kinetics analyses together with density functional theory calculations reveal that the square planar iron-bis(dihydroxy) (Fe-O-4) species in Fe-2-O-8-PcCu are responsible for the binding of polyiodide to restrain its dissolution into electrolyte. Besides the monovalent Na-I-2 batteries in organic electrolytes, the Fe-2-O-8-PcCu/I-2 cathode also operates stably in other metal-I-2 batteries like aqueous multivalent Zn-I-2 batteries. Thus, this work offers a new strategy for designing stable cathode materials toward high-performance metal-iodine batteries