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A Survey on Different Approaches used for Credit Card Fraud Detection

by Anika Nahar, Sharmistha Roy, Syeda Shabnam Hasan
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 4
Year of Publication: 2016
Authors: Anika Nahar, Sharmistha Roy, Syeda Shabnam Hasan
10.5120/ijais2016451492

Anika Nahar, Sharmistha Roy, Syeda Shabnam Hasan . A Survey on Different Approaches used for Credit Card Fraud Detection. International Journal of Applied Information Systems. 10, 4 ( January 2016), 29-34. DOI=10.5120/ijais2016451492

@article{ 10.5120/ijais2016451492,
author = { Anika Nahar, Sharmistha Roy, Syeda Shabnam Hasan },
title = { A Survey on Different Approaches used for Credit Card Fraud Detection },
journal = { International Journal of Applied Information Systems },
issue_date = { January 2016 },
volume = { 10 },
number = { 4 },
month = { January },
year = { 2016 },
issn = { 2249-0868 },
pages = { 29-34 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number4/855-2016451492/ },
doi = { 10.5120/ijais2016451492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:02:27.661867+05:30
%A Anika Nahar
%A Sharmistha Roy
%A Syeda Shabnam Hasan
%T A Survey on Different Approaches used for Credit Card Fraud Detection
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 4
%P 29-34
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now-a-days the use of credit card has dramatically increased due to rapid growth of e-commerce technology. It is the most popular mode of payment for both online as well as regular purchase.Credit card fraud has become a significant problem, as companies, banks other financial institutions faces huge amount of losses annually because of fraudulent activities of fraudsters. So the main aim of credit card fraud detection system is to learn different patterns used in previous frauds and train itself to identify fraudulent and non fraudulent transactions. In this paper a survey on different techniques used in credit card fraud detection such as Neural Network,Bayesian Network, Decision Trees, Hidden Markov Model, Support Vector Machines, Meta Learning, Blast-SSaha Algorithm, Fuzzy System with Neural Network, Fuzzy Darwinian System, and Genetic Algorithm is demonstrated.

References
  1. SuvasiniPanigrahi, AmlanKundu, ShamikSural, A.K. Majumdar.2009. “Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning”, Information Fusion 10.
  2. M. Ester, H.P. Kriegel, J. Sander, X. Xu.1996.“A density-based algorithm for discovering clusters in large spatial databases with noise” .Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD).
  3. V. Hodge, J. Austin.2004. “A survey of outlier detection methodologies”. Journal of Artificial Intelligence Review 22 (2).
  4. Visualizing DBSCAN Clustering. http://www.naftaliharris.com/blog/visualizing-dbscan-clustering/
  5. NehaSethi, Anju Gera. 2014 . “A Revived Survey of Various Credit Card Fraud Detection Techniques”.
  6. Y. Sahin and E. Duman, “Detecting Credit Card Fraud by Decision Trees and Support Vector Machines”.
  7. Cortes, C., Vapnik, V. 1995. “Support vector network. Machine Learning”. Vol: 20 pg.(273–297).
  8. Chen, R.-C., Luo, S.-T., Liang, X. and Lee, V. C. S. 2005. “Personalized approach based on SVM and ANN for detecting credit card fraud”.
  9. V. Dheepa and R. Dhanapal. “Behavior based Credit Card Fraud Detection using Support Vector Machines”.
  10. AvinashIngole, Dr. R. C. Thool. 2013. “Credit Card Fraud Detection Using Hidden Markov Model and Its Performance”, Volume 3, Issue 6, International Journal of Advanced Research in Computer Science and Software Engineering.
  11. A. K. Jain, M. N. Murty, and P.J Flynn. “Data Clustering: A Review”. ACM comput. Surv.,31(3):263-323 September 1999
  12. J.A.“A gentle tutorial of the EM algorithm and its application to Parameter Estimation for Gaussian mixture and Hidden Markov Models”.
  13. Ganesh Kumar.Nune† and P.VasanthSena . “Novel Artificial Neural Networks and Logistic Approach for Detecting Credit Card Deceit”.
  14. Philip K. Chan and Salvatore J. Stolfo (1993), Toward Parallel and Distributed Learning by Meta-learning, in Working Notes AAAI Work. Knowledge Discovery in Databases (pp. 227-240)
  15. Brazdil, P., Giraud-Carrier, C., Soares, C., and Vilalta, R. 2008.Metalearning: Applications to Data Mining, Berlin: Springer-Verlag.
  16. Vilalta, R., and Drissi, Y. 2002. “A Perspective View and Survey of Meta-Learning,” Artificial Intelligence Review (18), pp. 77-95.
  17. Salvatore J. Stofo.Credit Card Fraud Detection Using Meta-Learning:Issues and Initial Results.
  18. W. Buntime and R. Caruana .1991. Introduction to IND and Recursive Partitioning, NASAA Research Center.
  19. Philip K. Chan and Salvatore J. Stolfo .1997. Metrics for Analyzing the Integration of Multiple Learned Classifiers
  20. William W. Cohen .1995. Fast Effective Rule Induction,in Machine Learning: Proceeding of the Twelfth International Conference, Lake Taho, California, 1995.
  21. BhaviyaRajesh Gandani. “Credit Card Fraud Detection Using BLAST-SSAHA Hybridization & Hidden Markov Model”.
  22. Avanti H. Vaidya , S. W. Mohod. “Internet Banking Fraud Detection using HMM and BLAST-SSAHA Hybridization”.
  23. AmlanKundu, SuvasiniPanigrahi, ShamikSural and Arum K. Majumdar. 2009. “BLAST-SSAHA hybridization for credit card fraud detection”, IEEE Transactions on Dependable and Secure Computing, Vol. 6, No. 4.
  24. Eugene Charniak.1991. “Bayesians networks without tears”. AI Magazine.
  25. 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.
  26. Sherly K.K .2012. “A comparative assessment of supervised data mining techniques for fraud prevention”. TIST.Int.J.Sci.Tech.Res, Vol.1.
  27. Manoel Fernando, Xidi Wang, Alair Pereira do Lago.2008. “Comparison with Parametric Optimization in Credit Card Fraud Detection”.
  28. Varunchandola, Arindambanerjee, and Vipinkumar. “Anomaly Detection: A Survey. ACM Computing Surveys, Vol. 41, No. 3.
  29. J Han, M Kamber, J Pei. 2011. “Data Mining Concepts and Techniques”.
  30. RaghavendraPatidar, Lokesh Sharma “Credit Card Fraud Detection using Neural Network” International journal of Soft Computing(IJCSE),Volume 32,38,Issue 2011..
  31. DiptiThakur,Shalini Bhatia. 2002. “Distribution Data Mining approach to Credit card Fraud Detection”. SPIT IEEE Colloquium and International Conference, Volume 4.
  32. Suman,ResearchScholar,GJUS&THisar ,HCE Sonepat ,MitaliBansal, Mtech. C.S.E, HCE Sonepat“Survey Paper on Credit Card Fraud Detection ”
  33. vladimirzaslavsky and annastrizhak"Credit card fraud detection using self organizing maps".
  34. http://www.scholarpedia.org/article/Fuzzy_neural_network
  35. MubeenaSyeda, Yan-Qing Zhang and Yi Pan “Parallel Granular Neural Networks for Fast Credit Card Fraud Detection”. In: Proceedings of the IEEE international conference (2002). vol 1; (572–577).
  36. MasoumehZareapoor, Seeja.K.R, M.Afshar.Alam„Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria‟, International Journal of Computer Applications (0975 – 8887) Volume 52– No.3, August 2012.
  37. Peter J. Bentley, *Jungwon Kim, **Gil-Ho Jung and ***Jong-Uk Choi, “Fuzzy Darwinian Detection of Credit Card Fraud”.
  38. K.RamaKalyani, D.UmaDevi, “Fraud Detection of Credit Card Payment System by Genetic Algorithm”.
  39. SATVIK VATS*, SURYA KANT DUBEY, NAVEEN KUMAR PANDEY,“Genetic algorithms for credit card fraud Detection”.
  40. EkremDuman, M. HamdiOzcelik “Detecting credit card fraud by genetic algorithm and scatter search”. Elsevier, Expert Systems with Applications, (2011). 38; (13057–13063).
  41. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Credit Card Fraud Neural Network Bayesian Network Decision trees Support Vector Machine Genetic Algorithm Hidden Markov Model Meta Learning Fuzzy Darwinian System.

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