Combination of multiple detectors for credit card fraud detection

This paper presents a signal processing framework for the problem of automatic credit card fraud detection. This is a critical problem affecting large financial companies that has increased due to the rapid expansion of information and communication technologies. The framework establishes relationships between signal processing and pattern recognition issues around a detection problem with a very low ratio between fraudulent and legitimate transactions. Solutions are proposed using fusion of scores which are related to the familiar likelihood ratio statistic. Moreover, the classical detection problem analyzed by receiving operating characteristic curves is mapped to real-world business requirements based on key performance indicators. A strong practical case which combines real and surrogate data is approached, including comparison of the proposed methods with standard methods.

[1]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[2]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[3]  Antonio Soriano,et al.  Combination of multiple detectors for EEG based biometric identification/authentication , 2012, 2012 IEEE International Carnahan Conference on Security Technology (ICCST).

[4]  H. Vincent Poor,et al.  Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach , 2011, IEEE Transactions on Information Forensics and Security.

[5]  Kannan Ramchandran,et al.  Securing Dynamic Distributed Storage Systems Against Eavesdropping and Adversarial Attacks , 2010, IEEE Transactions on Information Theory.

[6]  David A. Montague.  Essentials of Online payment Security and Fraud Prevention , 2010 .

[7]  Mohan S. Kankanhalli,et al.  Multimodal fusion for multimedia analysis: a survey , 2010, Multimedia Systems.

[8]  Joseph T. Wells.  Corporate Fraud Handbook: Prevention and Detection , 2004 .

[9]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Timothy P. Hedley,et al.  Managing the Risk of Fraud and Misconduct: Meeting the Challenges of a Global, Regulated and Digital Environment , 2011 .

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

[12]  D.G. Tzikas,et al.  The variational approximation for Bayesian inference , 2008, IEEE Signal Processing Magazine.

[13]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  H. Poor,et al.  Utility-Privacy Tradeoff in Databases : An Information-theoretic Approach , 2013 .

[15]  Addisson Salazar,et al.  A general procedure for learning mixtures of independent component analyzers , 2010, Pattern Recognit..

[16]  Pramod K. Varshney,et al.  A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data , 2011, IEEE Transactions on Signal Processing.

[17]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[18]  Paulius Danenas.  Intelligent Financial Fraud Detection and Analysis: A Survey of Recent Patents , 2014 .

[19]  Antonio Soriano,et al.  Automatic credit card fraud detection based on non-linear signal processing , 2012, 2012 IEEE International Carnahan Conference on Security Technology (ICCST).

[20]  Antonio Soriano,et al.  On the fusion of non-independent detectors , 2016, Digit. Signal Process..

[21]  H. Vincent Poor,et al.  Privacy–Security Trade-Offs in Biometric Security Systems—Part I: Single Use Case , 2011, IEEE Transactions on Information Forensics and Security.

[22]  Robert P. W. Duin,et al.  Uniform Object Generation for Optimizing One-class Classifiers , 2002, J. Mach. Learn. Res..

[23]  H. Vincent Poor,et al.  Information and inference in the wireless physical layer , 2012, IEEE Wireless Communications.

[24]  Addisson Salazar,et al.  Surrogate techniques for testing fraud detection algorithms in credit card operations , 2014, 2014 International Carnahan Conference on Security Technology (ICCST).

[25]  Antonio Soriano,et al.  Fusion of Scores in a Detection Context Based on Alpha Integration , 2015, Neural Computation.

[26]  Joseph N. Wilson,et al.  Twenty Years of Mixture of Experts , 2012, IEEE Transactions on Neural Networks and Learning Systems.