Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Credit Card Fraud Detection using Time Series Analysis

Print
PDF
IJCA Proceedings on International Conference on Simulations in Computing Nexus
© 2014 by IJCA Journal
ICSCN - Number 3
Year of Publication: 2014
Authors:
Devaki. R
Kathiresan. V

Devaki. R and Kathiresan. V. Article: Credit Card Fraud Detection using Time Series Analysis. IJCA Proceedings on International Conference on Simulations in Computing Nexus ICSCN(3):8-10, May 2014. Full text available. BibTeX

@article{key:article,
	author = {Devaki. R and Kathiresan. V},
	title = {Article: Credit Card Fraud Detection using Time Series Analysis},
	journal = {IJCA Proceedings on International Conference on Simulations in Computing Nexus},
	year = {2014},
	volume = {ICSCN},
	number = {3},
	pages = {8-10},
	month = {May},
	note = {Full text available}
}

Abstract

Credit card usage has been increased tremendously because of the popularity of E-commerce. As the usage of credit card grows the occurrence of fraudulent transactions also increases, thus comes the stipulation of fraud detection. Detection of fraudulent transaction using credit card plays a vital role in financial institutions. In the proposed work, fraud detection is done with data mining approaches. The parameters considered are transaction amount and transaction time. For every cardholder there is always a robust periodic pattern in the spending behaviour, centered on this fact the anomalies in the transaction are detected by analyzing the past history of transactions belonging to an individual cardholder. In this work two levels of detection methods are used. At the first level the fraud is detected by analyzing whether the new incoming transaction is fraud or not by using distance-based method. At the second level the next transaction is predicted by means of label-prediction methodology and compared with the actual transaction, if there is deviation then it is detected to be a fraudulent transaction. If the particular transaction is considered as a fraud then the cardholder is asked to continue the transaction by asking a secret question, if the cardholder does not give correct answer then the transaction will not be allowed to continue further. The approach used in the proposed work has also decreased the false positive situation and hence it is ensured that genuine transaction is not rejected.

References

  • AbhinavSrivastava, AmlanKundu, ShamikSural and ArunK. Majumdar(2008) 'Credit Card Fraud Detection UsingHidden Markov Model' IEEE Transactions onDependable and Secure Computing vol. 5 No. 1.
  • Bhattacharya. S, Jha. S, Tharakunnel. k, and Westland J. C, "Data mining for credit card fraud: A comparative study. " Decision Support systems, Vol. 50, no. 7, 2011, pp. 602-613.
  • Clifton Phua, Vincent Lee, Kate Smith, Ross Gayler, 'A Comprehensive Survey of Data Mining-based Fraud DetectionResearch', http://arxiv. org/ftp/arxiv/papers/1009/1009. 6119. pdf.
  • Dipti Thakur and Shalini Bhatia (2009) 'Distributed Data Mining Approach to Credit Card Fraud Detection' Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India Vol. 4, 48.
  • Francisca NonyelumOgwueleka (2011) 'Data Mining Application in Credit Card Fraud Detection System' Journal of Engineering Science and Technology Vol. 6 No. 3.
  • Jiawei Han and MichelineKamber (2006) 'Data Mining Concepts and Techniques' ElseiverInc, Second edition ISBN:978-81-312-0535-8.
  • Lawrence R. Rabiner, 'A tutorial on Hidden Markov Models and Selected applications in Speech Recognition', Proceedings of the IEEE, VOL. 77, No 2, February 1989.
  • Leila Seyedhossein and Mahmoud Reza Hashemi (2010) 'A Timelier Credit card Fraud Detection by Mining Transaction Time series' International Journal of Information & Communication Technology vol 2 No 3.
  • Otto. P. E, Davies. G. B, Chater. N and Stott. H, 'From spending to understanding: Analyzing customers by their spending behaviour,' Journal of Retailing and Consumer Services, vol. 16, no. 1, 2009, pp. 10-18.
  • ParulBhanarkar and Pratiksha L. Meshram (2012) 'Credit and ATM card Detection using Genetic Approach' International Journal of Research & Technology Vol 1 Issue 10 ISSN:2278-0181.
  • RinkyD. Patel and Dheerajkumar Singh (2013) 'Credit Card Fraud Detection and Prevention of Fraud Using Genetic Algorithm' International Journal of Soft Computing and Engineering' ISSN:2231-2307, Vol-2, Issue-6.
  • 'A Tutorial on Clustering Algorithms', http://home. deib. polimi. it/matteucc/Clustering/tutorial_html/kmeans. html.
  • 'Data Clustering Algorithms', https://sites. google. com/site/dataclusteringalgorithms/k-means-clustering-algorithm.
  • 'Measures of distance between samples: Euclidean', http://www. econ. upf. edu/~michael/stanford/maeb4. pdf.