Detecting credit card fraud by genetic algorithm and scatter search

In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice.

[1]  Peter J. Bentley,et al.  Optimising the Performance of a Formula One Car Using a Genetic Algorithm , 2004, PPSN.

[2]  William F. Punch,et al.  PREDICTING STUDENT PERFORMANCE: AN APPLICATION OF DATA MINING METHODS WITH THE EDUCATIONAL WEB-BASED SYSTEM LON-CAPA , 2003 .

[3]  Xiaoyu Song,et al.  BDD minimization by scatter search , 2002, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[4]  P. Charbonneau.  Genetic algorithms in astronomy and astrophysics , 1995 .

[5]  Michael Levi,et al.  The nature, extent and economic impact of fraud in the UK , 2007 .

[6]  Jon T. S. Quah,et al.  Real-time credit card fraud detection using computational intelligence , 2008, Expert Syst. Appl..

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

[8]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

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

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[12]  Ingoo Han,et al.  The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms , 2003, Expert Syst. Appl..

[13]  Mehmet Kaya,et al.  Automated extraction of extended structured motifs using multi-objective genetic algorithm , 2010, Expert Syst. Appl..

[14]  Mehmet Kaya.  Autonomous classifiers with understandable rule using multi-objective genetic algorithms , 2010, Expert Syst. Appl..

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

[16]  Lothar Thiele,et al.  A Comparison of Selection Schemes used in Genetic Algorithms , 1995 .

[17]  F. Glover.  HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

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

[19]  Alair Pereira do Lago,et al.  Credit Card Fraud Detection with Artificial Immune System , 2008, ICARIS.