APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

Abstract

In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency -Frequency -Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object. Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98.

Enhanced Anomaly Detection in Imbalanced Credit Card Transactions using Hybrid PSO

“…A credit card fraud detection mechanism that performs the process of fraud detection using several intrinsic features from the characteristics of incoming transactions is presented in [2]. This technique uses customer's behavior patterns such as recency, frequency and monetary properties to predict frauds.…”

A comparative study of social network classifiers for predicting churn in the telecommunication industry

“…The final weights are obtained by aggregating all (w ij ) t for the whole time period. Weighted networks have been successfully used in credit card and social insurance fraud detection [28], [29].…”

“…Finally, in addition to CCP in telco, social network analysis has also been shown to give improved results in other areas, such as social network online games [27] and social security and credit card fraud [28], [29].…”

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

“…[2][3] [7] [10]. Véronique Van Vlasselaer and Cristián Bravo [5] has suggested the approach which combines inherent attributes derived from the characteristics of incoming transactions and the customer spending history using the primary characteristics such as RecencyFrequency-Monetary. Also, the network of credit card holders and merchants is taken into account to validate their relationship by calculating time-dependent suspiciousness score for each network object.…”