The Application of Hidden Markov Model in Credit Card Fraud Detection System
Keywords:
Credit Card, Hidden Markov Model (Hmm), Online Shopping, E-Commerce, Credit Card Fraud, Fraud Detection SystemAbstract
This work, credit card fraud detection system using Hidden Markov Model is based on card holder’s spending habits and can help in eradicating frauds that are associated with credit card transaction; this work thoroughly investigates every credit card transactions to ensure that any falsified transactions are restricted while ensuring that genuine card users are not denied transactions.
The Hidden Markov Model was applied in determining the spending habit and or the profile of credit card holders; more so, with the spending profile established, it becomes possible to determine if an incoming transaction from a card holder is fraudulent or not by comparing any new transaction with the credit card holder’s spending history while any variation from the actual spending habit is seen as a probable fraud and will be restricted and further verification is carried out. The methodology adopted for this research is Structured System Analysis and Design Methodology (SSADM). Data were collected and analyzed using PHP-MYSQL programming language for the design and test. The performance evaluation was designed to test the run-time performance of software within the context of an integrated system; this was cautiously carried out in all the testing process including unit and general testing. The performance of the software was justified since it met the aim and objective of the proposed system. Banks and other financial institutions that carry out their transactions with credit cards can adopt this system to detect and prevent all category of credit card fraud; the reliability and potential of this system to ward off credit card fraudsters is unquestionably high.
References
Aleskerov, E., Freisleben, B., AndRao. B( 1997) Card watch: A Neural Network Based
Database Mining System For Credit Card Fraud Detection, Proceedings of IEEE/Iafe: Computational Intelligence For Financial Eng. Pp. 220-226.
Bhattacharyya, S., Jha, S., TharakunneL K. & Westland. J. C. (2011), Data mining for credit card fraud: A comparative study. Decision Support Systems 50, pp. 602-613, DOI: 10.1016/j.dss.2010.08.008.
Chiu C., and Tsai, C., (2004). A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection, Proceedings of IEEE International Conference e-Technology, e-Commerce and e-Service. pp. 177-181.
Fan,W., Prodromidis, A. L., And Stolfo, S. J.. (1999). Distributed DataMiningIn Credit Card Fraud Detection, IEEE Intelligent Systems, Vol. 14. No. 5. Pp 67-74.
Ghosh, S. , Reilly, D.L. (1994). Credit Card Fraud Detection with a Neural- Network, Proc. 27th Hawaii Int’l Conf. System Sciences: Information Systems: Decision Support And Knowledge-Based Systems, Vol. 3, Pp. 621-630.
Kim, M..T. and Kim. T.S. 2002. A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection, Proc. International Conference on Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science, Springer Verlag, no. 2412, pp. 378-383.
Patil, D. D., Karad, S. M., Wadhai, V.M., Gokhale, J.A. & Halgacaiuir. P. S. (2010). Efficient Scalable Multi-Level Classification; scheme far Credit Card Fraud Detection, International Journal of Computer Science and Network Security. Vol. 10, No. 8, Pp. 17-18.
Phua, C., Lee. V.. Smith. K. and Gayler R (2007) . A comprehensive Survey of Data Mining based Fraud Detection Research, Pp. 190-200.
Phua, C., Alahakooo. D. and Lee, V.(2004). Minority Report in Fraud Detection
Classification of Skewed Data. ACMSIGKDD Explorations, Newsletter, vol. 6. no 1, pp. 50-59.
Rabiner. L.R. (1989). A Tutorial on Hidden Markov Models and Selected Applications In Speech Recognition, Proceedings of The IEEE, Vol. 77, No. 2, Pp. 257-286.
Vatsa.Y..Sural, S. and Majumdar, A.K.(2005). A Game-theoretic Approach to Credit Card
FraudDetection, Proc. 1st International Conference on Information Systems Security, Lecture Notes in Computer Science, Springer Verlag, pp. 263-276.
