Fraud Detection

Nikitha Pradeep; A  Rengarajan

doi:10.65301/iitm.2021.12.1.490

Articles Volume 12 Issue 1 January-June 2021

DOI 10.65301/iitm.2021.12.1.490

Fraud Detection

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57 Views

39 Downloads

Published 2021-01-30

Pages 81-82

Abstract

One of the most commonly used payment methods nowadays is the credit card payment method. It plays a very important role in today’s economy. In order to detect such frauds, the credit card fraud detection system has been introduced. The aim of fraud detection system is to detect fraudulent activities before it has commenced and it is necessary to identify the types of credit card fraud as the card can either be stolen by a person or the information can be misused for his personal use. A number of research works has been done to develop to solutions for detecting fraud activities. Modelling of the past data along with credit card transactions can be done to detect any fraudulent activities that has taken place.

Keywords

Credit card transactions anomaly detection Fraud detection

References

S. Ghosh and D. L. Reilly, “Credit card fraud detection with a neural-network,” in System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on, 1994, vol. 3: IEEE, pp. 621-630.
P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo, “Distributed data mining in credit card fraud detection,” IEEE Intelligent Systems and Their Applications, vol. 14, no. 6, pp. 67-74, 1999.
V. Zaslavsky, and Anna Strizhak., “Credit card fraud detection using self-organizing maps.,” Information and Security 18 2006
S. Ounacer, et al, “Using Isolation Forest in anomaly detection: the case of credit card transactions.,” Periodicals of Engineering and Natural Sciences (PEN) 6.2 (2018):, pp. 394-400, 2018
Y. Kou, C.-T. Lu, S. Sirwongwattana, and Y.-P. Huang, “Survey of fraud detection techniques,” in IEEE International Conference on Networking, Sensing and Control, 2004, 2004, vol. 2: IEEE, pp. 749-754.
C. Phua, et al., “”A comprehensive survey of data mining-based fraud detection research.”,” arXiv preprint arXiv:1009.6119 (2010).
A. Abdallah, Mohd Aizaini Maarof, and Anazida Zainal., “Fraud detection system: A survey,” Journal of Network and Computer Applications 68, pp. 90-113., 2016):
T. Fawcett and F. Provost, “Adaptive fraud detection,” Data mining and knowledge discovery, vol. 1, no. 3, pp. 291-316, 1997.
A. Ozgur, “Supervised and unsupervised machine learning techniques for text document categorization,” Unpublished Master’s Thesis, İstanbul: Boğaziçi University, 2004.
M. Rezapour, “Anomaly detection using unsupervised methods: credit card fraud case study,” Int J Adv Comput Sci Appl, vol. 10, no. 11, 2019.
Z. Ding and M. Fei, “An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window,” IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.
Z. Ding and M. Fei, “An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window,” IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.

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Nikitha Pradeep, A Rengarajan . (2021). Fraud Detection. IITM Journal of Management and IT, 12(1`), 81-82.

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IITM Journal of Management and IT

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[1] S. Ghosh and D. L. Reilly, “Credit card fraud detection with a neural-network,” in System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on, 1994, vol. 3: IEEE, pp. 621-630.

[2] P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo, “Distributed data mining in credit card fraud detection,” IEEE Intelligent Systems and Their Applications, vol. 14, no. 6, pp. 67-74, 1999.

[3] V. Zaslavsky, and Anna Strizhak., “Credit card fraud detection using self-organizing maps.,” Information and Security 18 2006

[4] S. Ounacer, et al, “Using Isolation Forest in anomaly detection: the case of credit card transactions.,” Periodicals of Engineering and Natural Sciences (PEN) 6.2 (2018):, pp. 394-400, 2018

[5] Y. Kou, C.-T. Lu, S. Sirwongwattana, and Y.-P. Huang, “Survey of fraud detection techniques,” in IEEE International Conference on Networking, Sensing and Control, 2004, 2004, vol. 2: IEEE, pp. 749-754.

[6] C. Phua, et al., “”A comprehensive survey of data mining-based fraud detection research.”,” arXiv preprint arXiv:1009.6119 (2010).

[7] A. Abdallah, Mohd Aizaini Maarof, and Anazida Zainal., “Fraud detection system: A survey,” Journal of Network and Computer Applications 68, pp. 90-113., 2016):

[8] T. Fawcett and F. Provost, “Adaptive fraud detection,” Data mining and knowledge discovery, vol. 1, no. 3, pp. 291-316, 1997.

[9] A. Ozgur, “Supervised and unsupervised machine learning techniques for text document categorization,” Unpublished Master’s Thesis, İstanbul: Boğaziçi University, 2004.

[10] M. Rezapour, “Anomaly detection using unsupervised methods: credit card fraud case study,” Int J Adv Comput Sci Appl, vol. 10, no. 11, 2019.

[11] Z. Ding and M. Fei, “An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window,” IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.

[12] Z. Ding and M. Fei, “An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window,” IFAC Proceedings Volumes, vol. 46, no. 20, pp. 12-17, 2013.