Authors
Kaneeka Joshi, Ranjeet K. Singh
Abstract
With the rise and light growth in e – commerce, the use of credit card for online transactions is also increasing dramatically. Due to this there is a great amount of increase in credit card frauds for which there is a requirement of various detection techniques for determining the fraudulent transactions. Frauds can either be offline or online for regular purchases, credit card is used as a mode of payment. Fraud is considered as the most ethical issue in credit card frauds and it is a million dollar business which is rising every year. Recent advances in techniques based on Data mining, Algorithm system (Genetic algorithm, Artificial Algorithm), Machine learning, Hidden Marokov model are the modern techniques that are introduced for detecting credit card fraudulent transactions. This paper reviews all the fraud detection techniques which have some advantages and disadvantages as well. As according to the study Hidden Markov model and Data mining techniques are considered as the best suitable techniques and may be considered over other techniques successfully. Key Words: Credit card, fraud detection, Data mining, Hidden Markov model, and e –commerce.
Introduction
Fraud generally refers to obtaining money and goods/services by wrongful or criminal deception or in illegal way which intends to result in personal or financial gain. Fraud deals with criminal events that needs identification which becomes difficult. Due to the development of technology and the increase of internet usage, credit card frauds or can say online frauds are increasing day by day. This wide ranging term “credit card fraud” used for theft or any fraud committed or any transaction that is done as to gain a fraudulent source of funds in a transaction. Credit card fraud is defined as when an individual uses another person’s credit card information for his/her personal use without the consent of the owner. These credit card frauds need immediate detection which should be active. In the cases of web technologies the research is necessary as it supports E-commerce in managing and building these applications. This business of E –commerce makes it possible to shop anything anytime as it have no time and geographical restrictions.
Detection of these frauds is difficult task if using normal process of detection, so the use of models i.e., fraud detection models is considered as of more importance in cases of academic or business organizations. E- Commerce are of many kinds – B2B (Business to Business), B2C (Business to Consumer), C2C (Consumer to Consumer) are the three popular application forms of E-commerce. As we have discussed number of models/ systems/ configuration/ process and some preventive measures that are used to avoid credit card fraud which reduces financial risks. These include some common cases such as acquiring or property trading which includes personal and intangible property such as stocks, bonds and copyrights. It is becoming essential to combat these type of fraudulent transactions for which various techniques were applied such as Hidden Markov Model, Artificial Intelligence, Sequence Alignment, Data Mining Techniques, Multiple cryptographic Algorithms and Genetic programming techniques (Rana and Baria, 2015; Meshram and Yenganti).
Types of Frauds
Credit card frauds are mainly divided into two classes:
• Offline frauds – are committed by using credit cards that are stolen at place.
• Online frauds – are committed via internet, shopping, phone, and web or in case of absence of credit card (Rana and Baria, 2015).
The definition of problems related to credit card frauds
An e-commerce business or system provides suitability for online transactions to shop anything to be a universal business. These frauds can be possible in both ways online as well as offline.
1. While online transactions such as during shopping, fraudster only uses the card information or just want to harass Merchant or to the banks by doing frauds:
2. In case if fraudster don’t want to buy anything from the shop but still he/she provides wrong information and payment was done via cash on delivery to harm the merchant.
3. If the credit card of the user was lost or stolen then the fraudster can use the information of card like Credit card number, CVV number to make payment without the consent of cardholder (Rana and Baria, 2015).
Proposed Systems for credit card fraud detection
Various credit card fraud techniques were developed by Researchers.
References
Alekhya, P. Phani, and Sk Mahaboob Basha. "Protecting E-Commerce Systems From Online Fraud." International Journal of Computer Trends and Technology (IJCTT) – 4.10 (2013): 3549-554. Web. 05 Apr. 2017.
Aleskerov,E., Freisleben, B. & B Rao. 1997., “CARDWATCH: A Neural Network –Based Database Mining System for Credit Card Fraud Detection”, Proc. Of the IEEE/IAFE on Computational Intelligence for Finance Engineering, 220-226.
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 ,Vol. 6, Issue no. 4, pp.309-315,October-December 2009S.
Bentley, P., Kim, J., Jung. G. & J Choi. 2000. Fuzzy Darwinian Detection of Credit Card Fraud, Proc. of 14th Annual Fall Symposium of the Korean Information Processing Society.
Brause R., Langsdorf T. & M Hepp. 1999a. Credit card fraud detection by adaptive neural data mining, Internal Report 7/99 (J. W. Goethe-University, Computer Science Department, Frankfurt, Germany).
Chaudhary , Khyati, Jyoti Yadav, and Bhawna Mallick. "A review of Fraud Detection Techniques: Credit Card ." International Journal of Computer Applications 45.1 (2012): 39-44. Web. 05 Apr. 2017.
Chaudhary, Khyati, and Bhawna Mallick. "Credit Card Fraud: Bang in E-Commerce." International Journal of Computational Engineering Research 2.3 (2012): 935-41. Web. 05 Apr. 2017.
Chaudhary, Khyati, and Bhawna Mallick. "Credit Card Fraud: The study of its Credit Card Fraud: The study of its Fraud: The study of its impact and detection techniques ." International Journal of Computer Science and Network 1.4 (2012): 31-35. Web. 05 Apr. 2017.
Ingole , Avinash , and R. C. Thool. "Credit Card Fraud Detection Using Hidden Markov Model and Its Performance." International Journal of Advanced Research in Computer Science and Software Engineering 3.6 (2013): 626-32. Web. 05 Apr. 2017.
Meshram , Pratiksha L., and Traun Yenganti. "Credit and ATM Card Fraud Prevention Using Multiple Cryptographic Algorithm." International Journal of Advanced Research in Computer Science and Software Engineering 3.8 (2013): 1300-305. Web. 05 Apr. 2017.
Nimisha Philip, Sherly K.K, “Credit Card Fraud Detection Based on Behaviour Mining” TIST.Int.J.Sci.Tech.Res., Vol.1 , 2012, pp. 7- 12.
Philip, Nimisha, and Sherly K. K. "Credit Card Fraud Detection Based on behavior mining." TIST.Int.J.Sci.Tech.Res 1 (2012): 7-12. Web. 05 Apr. 2017.
QUINLAN, J. R. (1993): C4.5: Program for machine learning. Morgan Kaufmann, San Mateo, CA, USA.
Rana , Priya J., and Jwalant Baria. "A Survey on Fraud Detection Techniques in Ecommerce." International Journal of Computer Applications 113.14 (2015): 5-7. Web. 05 Apr. 2017.
Reddy, P. Amarnath , and K. Srinivas. "Credit Card Fraud Detection and Alerting Using Hidden Mark Over Model And Sms Gateway." International Journal of Engineering Research & Technology 1.8 (2012): 1-7. Web. 05 Apr. 2017
S. Ghosh and D.L. Reilly, “Credit Card Fraud Detection with a Neural-Network,” Proc. 27th Hawaii Int’l Conf. System Sciences:Information Systems: Decision Support and KnowledgeBased Systems,vol. 3, pp. 621-630, 1994.
S. Stolfo and A.L. Prodromidis, “Agent-Based Distributed Learning Applied to Fraud Detection,” Technical Report CUCS-014-99, Columbia Univ., 1999.
S.S. Joshi and V.V. Phoha, “Investigating Hidden Markov Models Capabilities in Anomaly Detection,” Proc. 43rd ACM Ann. Southeast Regional Conf., vol. 1, pp. 98-103, 2005.
Srivastava, Abhinav, Kundu, Amlan, Sural, Shamik and Majumdar, Arun K., (2008) “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transactions on Dependable and Secure Computing, Vol. 5, No. 1, pp. 37-48.
Syed, Shabbir Ahsan, and R. Kannadasan. "An Effective Fraud Detection System Using Mining Technique." An Effective Fraud Detection System Using Mining Technique 3.5 (International Journal of Scientific and Research Publications): 1-4. Web. 05 Apr. 2017.
Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572- 577 (2002).
T. Lane, “Hidden Markov Models for Human/Computer Interface Modeling,” Proc. Int’l Joint Conf. Artificial Intelligence, Workshop Learning about Users, pp. 35-44, 1999.
W. Fan, A.L. Prodromidis, and S.J. Stolfo, “Distributed Data Mining in Credit Card Fraud Detection,” IEEE Intelligent Systems, vol. 14, no. 6, pp. 67-74, 1999.
X.D. Hoang, J. Hu, and P. Bertok, “A Multi-Layer Model for Anomaly Intrusion Detection Using Program Sequences of System Calls,” Proc. 11th IEEE Int’l Conf. Networks, pp. 531-536, 2003.
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