Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine

To deal with credit card fraud, a detection model based on Class Weighted Support Vector Machine was established. Due to large-scale and high dimensions of data, Principal Component Analysis (PCA) was adopted firstly to screen out the main factors from a great deal of indicative attributes in order to reduce the training dimension of SVM effectively. Then according to the characteristics of credit card transactions data which are imbalance, an improved SVM--Imbalance Class Weighted SVM (ICW-SVM) was adopted. With the application and verification in real dataset from bank, it is demonstrated that this model is more suitable for solving credit card fraud detection problem with higher precision and effective than others.

[1]  Suk-Hoon Chung,et al.  Estimating the utility value of individual credit card delinquents , 2009, Expert Syst. Appl..

[2]  J. M. Serrano,et al.  Association rules applied to credit card fraud detection , 2009, Expert Syst. Appl..

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

[4]  Edward Y. Chang,et al.  Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  Michael G. Madden,et al.  The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data , 2005, Knowl. Based Syst..

[7]  Hui-Chung Yeh,et al.  An Empirical Study of Credit Scoring Model for Credit Card , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[8]  Jonathan Crook,et al.  Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..

[9]  Oguz Findik,et al.  Effects of principle component analysis on assessment of coronary artery diseases using support vector machine , 2010, Expert Syst. Appl..

[10]  Weili. Ong,et al.  Real time credit card fraud detection using computational intelligence , 2011 .

[11]  Qingtian Zeng,et al.  An incremental learning algorithm for Lagrangian support vector machines , 2009, Pattern Recognit. Lett..

[12]  Li Xiu.  Support Vector Machine and Its Application in Customer Churn Prediction , 2007 .