Automatic credit card fraud detection based on non-linear signal processing

Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result presentation. Discriminant-based classifiers and an advanced non-Gaussian mixture classification method are employed to distinguish between legitimate and fraudulent transactions. The posterior probabilities produced by classifiers are fused by means of order statistical digital filters. Results from data mining of a large database of real transactions are presented. The feasibility of the proposed method is demonstrated for several datasets using parameters derived from receiver characteristic operating analysis and key performance indicators of the business.

[1]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[2]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

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

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  Brett Ninness,et al.  Generalised Hammerstein–Wiener system estimation and a benchmark application , 2012 .

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

[7]  Re nu,et al.  Analysis on Credit Card Fraud Detection Methods , 2014 .

[8]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[9]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[10]  Addisson Salazar,et al.  ICA Mixtures Applied to Ultrasonic Nondestructive Classification of Archaeological Ceramics , 2010, EURASIP J. Adv. Signal Process..

[11]  John W. Fisher,et al.  ICA Using Spacings Estimates of Entropy , 2003, J. Mach. Learn. Res..

[12]  Addisson Salazar,et al.  Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals , 2010 .

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

[14]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

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

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

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

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  W FisherJohn,et al.  ICA using spacings estimates of entropy , 2004 .

[20]  Shamik Sural,et al.  Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning , 2009, Inf. Fusion.

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

[22]  Addisson Salazar.  On statistical pattern recognition in independent component analysis mixture modelling , 2012 .