A graph-based, semi-supervised, credit card fraud detection system

Global card fraud losses amounted to 16.31 Billion US dollars in 2014 [18]. To recover this huge amount, automated Fraud Detection Systems (FDS) are used to deny a transaction before it is granted. In this paper, we start from a graph-based FDS named APATE [28]: this algorithm uses a collective inference algorithm to spread fraudulent influence through a network by using a limited set of confirmed fraudulent transactions. We propose several improvements from the network data analysis literature [16] and semi-supervised learning [9] to this approach. Furthermore, we redesigned APATE to fit to e-commerce field reality. Those improvements have a high impact on performance, multiplying Precision@100 by three, both on fraudulent card and transaction prediction. This new method is assessed on a three-months real-life e-commerce credit card transactions data set obtained from a large credit card issuer.

[1]  Cesare Alippi,et al.  Credit card fraud detection and concept-drift adaptation with delayed supervised information , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[2]  Kevin Françoisse,et al.  Semisupervised Classification Through the Bag-of-Paths Group Betweenness , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Kenji Fukumizu,et al.  Localized Centering: Reducing Hubness in Large-Sample Data , 2015, AAAI.

[4]  김태은,et al.  금융 Fraud Detection System 운영 프레임워크 연구 , 2015 .

[5]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[6]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[7]  Gianluca Bontempi,et al.  Adaptive Machine Learning for Credit Card Fraud Detection , 2015 .

[8]  Steven Kelk,et al.  Improving Card Fraud Detection Through Suspicious Pattern Discovery , 2017, IEA/AIE.

[9]  Véronique Van Vlasselaer,et al.  Fraud Analytics : Using Descriptive, Predictive, and Social Network Techniques:A Guide to Data Science for Fraud Detection , 2015 .

[10]  Xiaojin Zhu,et al.  Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.

[11]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[12]  François Fouss,et al.  An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification , 2012, Neural Networks.

[13]  Chris Arney.  Network Analysis: Methodological Foundations , 2012 .

[14]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[15]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..

[16]  Kate Smith-Miles,et al.  A Comprehensive Survey of Data Mining-based Fraud Detection Research , 2010, ArXiv.

[17]  Vladimir Zaslavsky,et al.  Credit Card Fraud Detection Using Self-Organizing Maps , 2006 .

[18]  D. Hand,et al.  Unsupervised Profiling Methods for Fraud Detection , 2002 .

[19]  Monique Snoeck,et al.  APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions , 2015, Decis. Support Syst..

[20]  Alexandros Nanopoulos,et al.  Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data , 2010, J. Mach. Learn. Res..

[21]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[22]  Niall M. Adams,et al.  Plastic card fraud detection using peer group analysis , 2008, Adv. Data Anal. Classif..

[23]  Gianluca Bontempi,et al.  Learned lessons in credit card fraud detection from a practitioner perspective , 2014, Expert Syst. Appl..

[24]  Marco Saerens,et al.  Semi-supervised classification and betweenness computation on large, sparse, directed graphs , 2011, Pattern Recognit..

[25]  Alexandros Nanopoulos,et al.  On the existence of obstinate results in vector space models , 2010, SIGIR.

[26]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .