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Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

by Masoumeh Zareapoor, Seeja.k.r, M. Afshar Alam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 3
Year of Publication: 2012
Authors: Masoumeh Zareapoor, Seeja.k.r, M. Afshar Alam
10.5120/8184-1538

Masoumeh Zareapoor, Seeja.k.r, M. Afshar Alam . Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria. International Journal of Computer Applications. 52, 3 ( August 2012), 35-42. DOI=10.5120/8184-1538

@article{ 10.5120/8184-1538,
author = { Masoumeh Zareapoor, Seeja.k.r, M. Afshar Alam },
title = { Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 3 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number3/8184-1538/ },
doi = { 10.5120/8184-1538 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:52.132363+05:30
%A Masoumeh Zareapoor
%A Seeja.k.r
%A M. Afshar Alam
%T Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 3
%P 35-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria. And this survey enables us to build a hybrid approach for developing some effective algorithms which can perform well for the classification problem with variable misclassification costs and with higher accuracy.

References
  1. A. Vellidoa, P. J. G. Lisboaa, J. Vaughan (1999). "Neural networks in business: a survey of applications". Elsevier, Expert Systems with Applications, 17; (51–70).
  2. A. J. Graaff A. P. Engelbrecht (2011). "The Artificial Immune System for Fraud Detection in the Telecommunications Environment"; (1-4)
  3. Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun K. Majumdar (2008). "Credit Card Fraud Detection using Hidden Markov Model". IEEE Transactions on dependable and secure computing,Volume 5; (37-48).
  4. Aihua Shen, Rencheng Tong, Yaochen Deng (2007). "Application of Classification Models on Credit Card Fraud Detection".
  5. Anshul Singh, Devesh Narayan (2012). "A Survey on Hidden Markov Model for Credit Card Fraud Detection". International Journal of Engineering and Advanced Technology (IJEAT), Volume-1, Issue-3; (49-52).
  6. B. Sanjaya Gandhi , R. Lalu Naik, S. Gopi Krishna, K. lakshminadh (2011). "Markova Scheme for Credit Card Fraud Detection". International Conference on Advanced Computing, Communication and Networks; (144-147).
  7. Bidgoli, B. M. , Kashy, D. , Kortemeyer, G. & Punch, W. F. (2003). "Predicting student performance: An Application of data mining methods with the educational web-based system LON-CAPA". In Proceedings of ASEE/IEEE frontiers in education conference.
  8. Bolton, R. J. , Hand, D. J (2002). "Statistical fraud detection: A review". Statistical Science 28(3); (235—255).
  9. Clifton Phua, Vincent Lee, Kate Smith, and Ross Gayler (2005). "A comprehensive survey of data mining-based fraud detection research". In Artificial Intelligence Review.
  10. Cortes, C. & Vapnik, V. (1995). "Support vector networks, Machine Learning". Vol. 20; (273–297).
  11. De Castro Silva, L. N. , & Zuben, F. J. V. (2000). "An evolutionary immune network for data clustering". In Proceedings of the IEEE SBRN (Brazilian Symposium on Artificial Neural Networks); (84–89).
  12. De Castro, L. , & Timmis, J. (2002). "Artificial immune systems: a new computational approach". London, UK: Springer-Verlag.
  13. De Castro, L. N. & Von Zuben, F. J. (1999a). "Artificial immune systems", part i – basic theory and applications. Technical Report, Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering.
  14. De Castro, L. N. & Von Zuben, F. J. (1999b). "Artificial immune systems", part ii – a survey of applications. Technical Report, Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering.
  15. Dipti D. Patil, V. M. Wadhai, J. A. Gokhale (2010). "Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy". International Journal of Computer Applications, Volume 11– No. 2; (23-30).
  16. E. W. T. Ngai, Yong Hu, Y. H. Wong, Yijun Chen, Xin Sun (2011). "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature". Elsevier-Decision Support Systems 50; (559–569).
  17. Ekrem Duman, M. Hamdi Ozcelik (2011). "Detecting credit card fraud by genetic algorithm and scatter search". Elsevier, Expert Systems with Applications, 38; (13057–13063).
  18. Eugene Charniak (1991). "Bayesians networks without tears". AI Magazine.
  19. Forrest, S. , Perelson, A. S. , Llen, L. & Cherukuri, R. (1994). "Self-nonself discrimination in a computer". Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy; (202–212).
  20. Francisca nonyelum ogwueleka (2011). "Data Mining Application in Credit Card Fraud Detection System". Journal of Engineering Science and Technology Vol. 6, No. 3; (311 – 322).
  21. Ganesh K. Venayagamoorthy (2004). "TEACHING NEURAL NETWORKS CONCEPTS AND THEIR LEARNING TECHNIQUES". Proceedings of the American Society for Engineering Education Midwest Section Conference.
  22. Ghosh, D. L. Reilly (1994). "Credit Card Fraud Detection with a Neural- Network". Proceedings of the International Conference on System Science; (621-630).
  23. H. Leggatt, CyberSource (2008). "Online fraud to reach $4 billion". BizReport, December 16.
  24. Hamid Farvaresh, Mohammad Mehdi Sepehri (2011). "A data mining framework for detecting subscription fraud in telecommunication". Engineering Applications of Artificial Intelligence, 24; (182–194).
  25. Hiotis, A. (1993). "Inside a self-organizing map". AI Expert, 8(4); (38-43).
  26. Hofmeyr, S. A. & Forrest, S. (1999b). "Immunity by design: an artificial immune system". Proceedings of the Genetic and Evolutionary Computation Conference (GECCO); (1289–1296).
  27. Holland, J. H. (1975). "Adaptation in natural and artificial systems. " Ann Arbor: The University of Michigan Press.
  28. J. Hunt, J. Timmis, D. Cooke, M. Neal, C. King (1998). "Development of an artificial immune system for real-world applications". Artificial Immune Systems and their Applications, Springer; (157–186).
  29. Jon T. S. Quah, M. Sriganesh (2008). "Real-time credit card fraud detection using computational intelligence". Elsevier, Expert Systems with Applications, 35; (1721–1732).
  30. Joseph King-Fung Pun (2011). "Improving Credit Card Fraud Detection using a Meta-Learning Strategy". A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto.
  31. Kim, M. , & Han, I. (2003). The discovery of experts' decision rules from qualitative bankrupcy data using genetic algorithms. Elsevier, Expert Systems with Applications, 25; (637–646).
  32. Kohonen, T (1990). "The self-organizing map". In Proceedings of the IEEE 78 (9); (1464–1480).
  33. Kou, Y. , Lu, C. -T. , Sirwongwattana, S. , Huang, Y. -P (2004). "Survey of fraud detection techniques". In Proceedings of the IEEE International Conference on Networking, Sensing and Control.
  34. L. N. de Castro, J. Timmis (2002). "Artificial Immune Systems". A New Computational Intelligence Approach, Springer.
  35. Lean Yu a, Wuyi Yue, Shouyang, Wang, K. K. Lai (2010). "Support vector machine based multiagent ensemble learning for credit risk evaluation". Expert Systems with Applications 37; (1351–1360).
  36. Leila Seyedhossein, mahmoud reza hashemi (2010). "Mining Information from Credit Card Time Series for Timelier Fraud Detection". IEEE-5th International Symposium on Telecommunications.
  37. M. Jeevana Sujitha, 2K. Rajini Kumari, 3N. Anuragamayi (2012). "The Credit Card Fraud Detection Analysis With Neural Network Methods". IJCST Vol. 3, Issue 1; (959-963).
  38. Malak Alshawabkeh, Byunghyun Jang, David Kaeli (2010). " Accelerating the Local Outlier Factor Algorithm on a GPU for Intrusion Detection Systems". ACM; (104-110).
  39. Manoel Fernando, Xidi Wang, Alair Pereira do Lago (201). "Comparison with Parametric Optimization in Credit Card Fraud Detection".
  40. Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi, Maher A. Sid-Ahmed (2007). "Investigating the Performance of Naive- Bayes Classifiers and K- NearestNeighbor Classifiers". IEEE, International Conference on Convergence Information Technology; (1541-1546).
  41. Mubeena Syeda, Yan-Qing Zhang and Yi Pan (2002). "Parallel Granular Neural Networks for Fast Credit Card Fraud Detection". In: Proceedings of the IEEE international conference vol 1; (572–577).
  42. Mu-Chen Chena, Shih-Hsien Huang (2003). "Credit scoring and rejected instances reassigning through evolutionary computation techniques". Elsevier, Expert Systems with Applications 24; (433–441).
  43. Mukhanov (2008). "Using bayesian belief networks for credit card fraud detection". in Proc. of the IASTED International conference on Artificial Intelligence and Applications; (221– 225).
  44. N. Cristianini, J. Shawe-Taylor (2000). "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". Cambridge University Press.
  45. Nicholas Wong, Pradeep Ray, Greg Stephens & Lundy Lewis (2012). "Artificial immune systems for the detection of credit card fraud". Info Systems, Volume 22; (53–76).
  46. P. K. Chan, W. Fan, A. L. Prodromidis, S. J. Stolfo (1999). "Distributed Data Mining in Credit Card Fraud Detection". Data Mining; (67–74).
  47. Peter J. Bentley, Jungwon Kim, Gil-Ho Jung and Jong-Uk Choi (2000). "Fuzzy Darwinian Detection of Credit Card Fraud". In the 14th Annual Fall Symposium of the Korean Information Processing Society; (1-4).
  48. Phua, C, Lee, V. , Smith, K. , Gayler, R (2002). "A comprehensive survey of data mining-based fraud detection research". Artificial Intelligence Review.
  49. Qibei Lu, Chunhua Ju (2011). "Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine". Journal of Convergence Information Technology, Volume 6, Number 1; (62-68).
  50. R. Brause, T. Langsdorf, M. Hepp (1999). "Neural Data Mining for Credit Card Fraud Detection, "International Conference on Tools with Artificial Intelligence; (103-106).
  51. R. C. Chen, T. S. Chen, C. C. Lin (2006). "A new binary support vector system for increasing detection rate of credit card fraud". International Journal of Pattern Recognition 20 (2); (227–239).
  52. Raghavendra Patidar, Lokesh Sharma (2011). "Credit Card Fraud Detection Using Neural Network". International Journal of Soft Computing and Engineering (IJSCE), Volume-1, Issue; (32-38).
  53. Raghuveer Kancherla, Ratna Venkata, Anurag Verma (2008). "Behavioral Fraud Mitigation through Trend Offsets". (1-11).
  54. Ray-I Chang, Liang-Bin Lai, Wen-De Su, Jen-Chieh Wang, Jen-Shiang Kouh (2006). "Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query". Research India Publications; (6-10).
  55. Rekha Bhowmik (2011). "Detecting Auto Insurance Fraud by Data Mining Techniques". Journal of Emerging Trends in Computing and Information Sciences, Volume 2 No. 4; (156-162).
  56. S. Benson Edwin Raj, A. Annie Portia (2011). "Analysis on Credit Card Fraud Detection Methods". IEEE-International Conference on Computer, Communication and Electrical Technology; (152-156).
  57. S. Maes, K. Tuyls, B. Vanschoenwinkel, B. Manderick (1993). "Credit card fraud detection using Bayesian and neural networks". Proceedings of the First International NAISO Congress on Neuro Fuzzy Technologies; (261-270).
  58. Sahin, Y. , Duman, E (2010). "An overview of business domains where fraud can take place and a survey of various fraud detection techniques". In Proceedings of the 1st International Symposium on Computing in Science and Engineering.
  59. Serrano-Cinca, C (1996). "Self-organizing neural networks for financial diagnosis". Decision Support Systems, 17; (227–238).
  60. Sherly K. K (2012). "A COMPARATIVE ASSESSMENT OF SUPERVISED DATA MINING TECHNIQUES FOR FRAUD PREVENTION". TIST. Int. J. Sci. Tech. Res. ,Vol. 1; (1-6).
  61. Shifei Ding • Hongjie Jia • Jinrong Chen • Fengxiang Jin (2012). "Granular neural networks". Springer Science+Business Media B. V.
  62. Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel, J. Christopher Westland (2011). "Data mining for credit card fraud: A comparative study". Elsevier, Decision Support Systems ,50; (602–613).
  63. Silvia Cateni, Valentina Colla, Marco Vannucci (2008). "Outlier Detection Methods for Industrial Applications". Advances in Robotics, Automation and Control; (265-282).
  64. Suvasini Panigrahi, Amlan Kundu, Shamik Sural, A. K. Majumdar (2009). "Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning". Elsevier, Information Fusion 10; (354–363).
  65. Tan, P. N. , Steinbach, M, And Kumar. V (2005). "Introduction to Data Mining".
  66. Tao Guo, Gui-Yang Li (2008). "NEURAL DATA MINING FOR CREDIT CARD FRAUD DETECTION". IEEE, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics; (3630-3634).
  67. Tatsuya Minegishi1, Ayahiko Niimi (2011). "Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality". International Journal for Information Security Research (IJISR), Volume 1, Issue 4; (229-235).
  68. V. Bhusari S. Patil (2011). "Study of Hidden Markov Model in Credit Card Fraudulent Detection". International Journal of Computer Applications, Volume 20– No. 5; (0975 – 8887).
  69. V. Dheepa, Dr. R. Dhanapal (2009). "Analysis of Credit Card Fraud Detection Methods". International Journal of Recent Trends in Engineering, Vol 2, No. 3; (126-128).
  70. Varun chandola, Arindam banerjee, and Vipin kumar (2009). "Anomaly Detection: A Survey. ACM Computing Surveys, Vol. 41, No. 3; (15-72).
  71. Vesanto, J. , & Alhoniemi, E. (2000). "Clustering of the self-organizing map". IEEE Transactions on Neural Networks, 11; (586–600).
  72. Vladimir ZASLAVSKY and Anna STRIZHAK (2006). "CREDIT CARD FRAUD DETECTION USING SELFORGANIZING MAPS". INFORMATION & SECURITY. An International Journal, Vol. 18; (48-63).
  73. W. B. Langdon, R. Poli (2002). "Foundations of Genetic Programming". Springer-Verlag, Berlin.
  74. Wightman, J. (2003). "Computer immune techniques in e-commerce fraud detection. " School of Information Systems and Technology Management, The University of New South Wales, Honours Thesis. .
  75. Wu, C. -H. , Tzeng, G. -H. , Goo, Y. -J. , & Fang, W. -C. (2007). "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy". Expert Systems with Applications, 32(2); (397–408).
  76. Yen-Hsien Lee, Chih-Ping Wei, Tsang-Hsiang Cheng, Ching-Ting Yang (2012). "Nearest-neighbor-based approach to time-series classification". Elsevier, Decision Support Systems 53; (207–217).
  77. Yok-Yen Nguwi, Siu-Yeung Cho (2010). "An unsupervised self-organizing learning with support vector ranking for imbalanced datasets". Expert Systems with Applications, 37; (8303–8312).
  78. Yuen, C. W. M. , Wong, W. K. , Qian, S. Q. , Chan, L. K. , & Fung, E. H. K. (2009). "A hybrid model using genetic algorithm and neural network for classifying garment defects". Expert Systems with Applications, 36(2); (2037–2047).
  79. Yufeng Kou, Chang-Tien Lu, Sirirat Sinvongwattana Yo-Ping Huang (2004). "Survey of Fraud Detection Techniques". Proceedings of the IEEE International Conference on Networking, Sensing & Control Taipei, Taiwan; (749-754).
Index Terms

Computer Science
Information Sciences

Keywords

credit card fraud fraud detection

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