Credit Card Fraud Detection using GSA

Authors(2) :-Ritu, Sudesh Nandal

Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is the key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. We found German credit card fraud detection database available publically which is having 1000 data points. This dataset is divided into 70/30 ratio for training and testing the neural network. The famous and efficient machine learning neural network algorithm is used to get a trained NN. This network is further updated for more classification accuracy using Gravitational Search Algorithm (GSA) which is an optimization algorithm. It tunes NN's weights and biases and check for the mean square error which is an evaluation parameter also in our work. Complete work is simulated in MATLAB R 2016a. Results are compared with previously used simulated annealing (SA) algorithm and proposed method is giving better results in term of area under curve (AUC) of ROC (receiver operating characteristics) and MSE.

Authors and Affiliations

Ritu
Department of Electronics and Communication Engineering, Bhagat Phool SinghMahila Vishwavidalaya Khanpur Kalan , Sonepat , Haryana, India
Sudesh Nandal
Department of Electronics and Communication Engineering, Bhagat Phool SinghMahila Vishwavidalaya Khanpur Kalan , Sonepat , Haryana, India

Keyword: Fraud Detection , Gravitational Search Algorithm, Neural Network, Simulated Annealing

  1. Raghavendra Patidar, Lokesh Sharma," Credit Card Fraud Detection Using Neural Network", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume1, Issue-NCAI2011, Page No. 32-38 June 2011
  2. V. Bhusari, S. Patil," Study of Hidden Markov Model in Credit Card Fraudulent Detection ", International Journal of Computer Applications (0975 - 8887) Volume 20-No.5, Issue-5, Page No. 33 April 2011
  3. Dr R.DHANAPAL , GAYATHIRI.P," Credit Card Fraud Detection Using Decision Tree For Tracing Email And Ip ", IJCSI International Journal of Computer Science Issues,Volume 9,Issue 5, No 2,Page No. 406-412 September 2012
  4. X.Y. Liu, J. Wu, and Z.H. Zhou. Exploratory under sampling for class-imbalance learning. Systems, Man, and Cybernetics, Part B: Cybernetics,Volume 39,No. 2, Page No. 539-550, 2009.
  5. Vijayshree B. Nipane, Poonam S. Kalinge, Dipali Vidhate, Kunal War, Bhagyashree P. Deshpande,"Fraudulent Detection in Credit Card System Using SVM & Decision Tree", IJSDR, Volume 1, Issue 5,Page No. 896-901 2016.
  6. Mohamed Hegazy, Ahmed Madian, Mohamed Ragaie,"Enhanced Fraud Miner: Credit Card Fraud Detection using Clustering Data Mining Techniques", Egyptian Computer Science Journal (ISSN: 1110-2586) Volume 40 - Issue 3, Page No. 72-81 September 2016
  7. Azeem Ush Shan Khan, Nadeem Akhtar and Mohammad Naved Qureshi,"Real-Time Credit-Card Fraud Detection using Artificial Neural Network Tuned by Simulated Annealing Algorithm", Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC, Page No. 114-121, 2014
  8. L.U. Oghenekaro, C. Ugwu,"A Novel Machine Learning Approach to Credit Card Fraud Detection ", International Journal of Computer Applications (0975 - 8887) Volume 140 - No.5, Page No. 45-50, April 2016
  9. Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K. Majumdar, "Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning," Special Issue on Information Fusion in Computer Security, Volume 10, Issue no 4, Page No. 54-363, October 2009
  10. Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K.Majumdar, "BLAST-SSAHA Hybridization for Credit Card Fraud Detection," IEEE Transactions On Dependable And Secure Computing, Volume 6, Issue no. 4, Page No.309-315, October-December 2009.
  11. Sunil Bhatia1, Rashmi Bajaj2, Santosh Hazari3, “Analysis of Credit Card Fraud Detection Techniques,” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013) Volume 5, Issue no. 3,Page No. 1302-1307,March 2016.
  12. L. Lei, "Card Fraud Detection by Inductive Learning and Evolutionary Algorithm," 2012 Sixth International Conference on Genetic and Evolutionary Computing, Kitakushu, 2012, Page No. 384-388.
  13. M. Lotfi Shahreza, “Anomaly detection using a self-organizing map and particle swarm optimization,” Volume 18, Issue 6, Pages 1460-1468, December 2011.
  14. Nader Mahmoudi , Ekrem Duman, “Detecting credit card fraud by Modi?ed Fisher Discriminant Analysis,” Expert Systems with Applications, Volume 42, Issue 5, , Pages 2510-2516,1 April 2015.
  15. Jarrod West and Maumita Bhattacharya, “Intelligent Financial Fraud Detection: A Comprehensive Review,” Computers & Security (2015),Volume 57, , Pages 47-66, March 2016.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 702-708
Manuscript Number : CSEIT1723236
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ritu, Sudesh Nandal, "Credit Card Fraud Detection using GSA", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.702-708, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT1723236

Article Preview

Follow Us

Contact Us