Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing

Vaibhav Lal, Siddharth Choubey


The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study, we present several models and algorithm used in data mining for the detection of such malicious fraudulent or thefts. Such algorithm learns the transaction patterns and clusters the pattern of sequences usually involving with the processing of transactions to inhibit such malicious transactions made in the future.

Keywords: Online transactions, credit card, credit card fraud, detection techniques, credit bureaux, data mining techniques, fraud detection

Cite this Article
Vaibhav Lal, Siddharth Choubey, A Survey of Fraud Detection Techniques for Credit Card Based Transaction
Processing, Recent Trends in Parallel Computing. 2015; 2(1): 10–15p.

Full Text:



Aleskerov E., Freisleben B. & B Rao CARDWATCH: A Neural Network-Based Database Mining System for Credit Card Fraud Detection. Proc. of the IEEE/IAFE on Computational Intelligence for Financial Engineering. 1997; 220–226p.

Anderson R.The Credit Scoring Toolkit: theory and practice for retail credit risk management and decision automation. New York: Oxford University Press. 2007.

APACS, Association for Payment Cleaning Services, no date. Card Fraud Facts and Figures Available at: (Accessed: December 2007).

Bellis M. no date. Who Invented Credit Cards-the History of Credit Cards? Available at: http://inventors.about. com/od/cstartinventions/a/credit_cards.htm (Accessed: October 2008).

Bentley P., Kim J., Jung. G. & J Choi. Fuzzy Darwinian Detection of Credit Card Fraud, Proc. of 14thAnnual Fall Symposium of the Korean Information Processing Society. 2000.

Bolton R. & Hand D. Statistical Fraud Detection: A Review. Statistical Science. 2002; 17: 235–249p.

Bolton R. & Hand D.Unsupervised Profiling Methods for Fraud Detection. Credit Scoring and Credit Control VII. 2001.

Brause R., Langsdorf T. & M Hepp. Credit card fraud detection by adaptive neural data mining, Internal Report 7/99 (J. W. Goethe-University, Computer Science Department, Frankfurt, Germany). 1999.

Brause R., Langsdorf T. & M Hepp. Neural Data Mining for Credit Card Fraud Detection. Proc. of 11thIEEE International Conference on Tools with Artificial Intelligence. 1999.

Caminer B. Credit card Fraud: The Neglected Crime. The Journal of Criminal Law and Criminology. 1985; 76: 746–763p.

Chan P., Fan W. Prodromidis A. & S Stolfo. Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems. 1999; 14: 67–74p.

Chan P., Stolfo S., Fan D.,et al.Credit card fraud detection using meta learning: Issues and initial results. Working notes of AAAI Workshop on AI Approaches to Fraud Detection and Risk Management. 1997.

Chepaitis E. Information Ethics Across Information Cultures. Business Ethics: A European Review. 1997; 6(4): 195–199p.

Chiu C. & Tsai C. A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection. Proc. of 2004 IEEE International Conference on e-Technology, e-Commerce and e- Service. 2004.

Clarke M. Fraud and the Politics of Morality. Business Ethics: A European Review. 1994; 3(2): 117–122p.

Dorronsoro J. Ginel, F. Sanchez, C. & C Cruz. Neural Fraud Detection in Credit Card Operations. IEEE Transactions on Neural Networks. 1997; 8: 827–834p.

Encyclopedia Britannica, no date. Credit Card. Available at: October 2008)

Euromonitor International, 2006. Financial cards in Germany Available at: (Accessed: November 2006).

European e-Business Market Watch. 2005. ICT Security, e-Invoicing and e-Payment Activities in European Enterprises, Special Report, September.

Ezawa K. & Norton S. Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts. IEEE Expert. 1996; 45–51p.

Fan W. Systematic Data Selection to Mine Concept-Drifting Data Streams. Proc. of SIGKDD04. 2004; 128–137p.

Fan W., Miller M., Stolfo S., et al. Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. Proc. of ICDM01. 2001; 123–248p.

Fawcett T. & Provost F. Adaptive Fraud Detection. Data Mining and Knowledge Discovery. 1997.

Foster D. & Stine R. Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy. Journal of American

Statistical Association. 2004; 99: 303–313p.

George E. Ethics in Banking. Business Ethics: A European Review. 1992; 1(3): 162– 171p.

Ghosh S. & Reilly D. Credit Card Fraud Detection with a Neural-Network. Proc. of 27th Hawaii International Conference on Systems Science. 1994; 3: 621–630p.

Gichure C. Fraud and the African Renaissance. Business Ethics: A European Review. 2000; 9(4): 236–247p.

ID Analytics. Identity 2004: The Identity Risk Management Conference. 2004.

Kim M. & Kim T. A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection. Proc. Of IDEAL. 2002; 378–383p.

Kokkinaki A. On Atypical Database Transactions: Identification of Probable Frauds using Machine Learning for User Profiling. Proc. of IEEE Knowledge and Data Engineering Exchange Workshop. 1997; 107–113p.

Leonard K.The development of a rule based expert system model for fraud alert in consumer credit. European Journal of Operational Research. 1995; 80: 350–356p.

Maes S., Tuyls K., Vanschoenwinkel B. & B Manderick. Credit Card Fraud Detection using Bayesian and Neural Networks. Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies. 2002.

Molyneaux D. Two case study scenarios in banking: a commentary on The Hutton Prize for Professional Ethics, 2004 and 2005. Business Ethics: A European Review. 2007; 16(4): 372–386p.

Oscherwitz T. Synthetic Identity Fraud: Unseen Identity Challenge, Bank Security News. 2005; 3(7).

Pago-Report. The development of E-commerce sectors, ©Pago eTransaction Services GmbH. 2005.

Pago-Report. Trends in Consumer Purchasing and Payment Behaviour in selected E-commerce Industries, ©Pago eTransaction Services GmbH. 2007.

Phua C., Alahakoon D., & V Lee. Minority Report in Fraud Detection: Classification of Skewed Data. ACM SIGKDD Explorations: Special Issue on Imbalanced Data Sets. 2004; 6: 50–59p.

Phua C., Gayler R., Lee V., & K Smith. On the Approximate Communal Fraud Scoring of Credit Applications. Proceedings of Credit Scoring and Credit Control IX. 2006.

Phua C., Lee V., Smith K. and Gayler R. A Comprehensive Survey of Data Mining-based Fraud Detection Research. Artificial Intelligence Review. 2005.

Quah T. S & Sriganesh M. Real-time credit card fraud using computational intelligence. Expert Systems with Application. 2008; 35(4): 1721–1732p.

Siddiqi N. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons, USA. 2006.

Thomas L.C., Edelman D.B., & J.N Crook. Credit Scoring and its Applications. SIAM Monographs on Mathematical Modeling and Computation,

Philadelphia. 2002.

Thomas L.C., Edelman D.B., & J.N Crook. Readings in Credit Scoring: Foundations, Developments, and Aims. Oxford University Press, USA. 2004.

Wheeler R. & Aitken S. Multiple Algorithms for Fraud Detection. Knowledge-Based Systems. 2000; 13: 93–99p.

Zaslavsky V. & Strizhak A. Credit card fraud detection using self-organizing maps. Information and Security. 2006; 18: 48–63p.


  • There are currently no refbacks.

This site has been shifted to