* Corresponding Author
Hide
Show all
Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)
https://doi.org/10.22146/ijccs.46561 Silvano Sugidamayatno(1*), Danang Lelono(2)
(1) Master Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%).
Keywords
Full Text:
PDFReferences
[1] E. Turban, J. E. Aronson, and Ting-Peng Liang, Decision Support Systems and Intelligent Systems, Upper Saddle River, NJ: Pearson Prentice Hall, 2007.
[2] V. Vaishali, "Fraud Detection in Credit Card by Clustering Approach", International Journal of Computer Applications, Vol. 98, no. 3, July 2014. Available: https://research.ijcaonline.org/volume98/number3/pxc3897225.pdf. [Accessed: October-10-2018].
[3] S. V. Bhosale, "A Survey: Outlier Detection in Streaming Data Using Clustering Approached", (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5, p. 6050 – 6053, 2014. Available: http://ijcsit.com/docs/Volume%205/vol5issue05/ijcsit2014050510.pdf. [Accessed: October-10-2018].
[4] M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data", PloS one, p. 1-31, April 2016. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152173. [Accessed: October-10-2018].
[5] J. Auskalnis, N. Paulauskas, and A. Baskys, "Application of local outlier factor algorithm to detect anomalies in computer network", Elektronika ir Elektrotechnika, vol. 24, p. 96– 99, 2018. Available: http://eejournal.ktu.lt/index.php/elt/article/view/20972. [Accessed: October-20-2018].
[6] J. Tang, H. Y. T. Ngan, J. Tang, and H. Y. T. Ngan, "Traffic Outlier Detection by Density Based Bounded Local Outlier Factors", HKBU Institutional Repository, vol. 1, p. 6 – 18, 2016. Available: https://repository.hkbu.edu.hk/cgi/viewcontent.cgi?article=7235&context=hkbu_staff_p ublication . [Accessed: October-10-2018].
[7] M. Breunig, Markus, Hans peter Kriegel, Raymond T Ng, and Jörg Sander, "LOF: Identifying Density-Based Local Outliers", Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, p. 1 - 2. Available: http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf. [Accessed: October-10-2018].
[8] R. Devaki, "Credit Card Fraud Detection Using Time Series Analysis", International Journal of Computer Applications, no. 3, p. 8 - 10, 2014. Available: https://www.ijcaonline.org/proceedings/icscn/number3/16159-1031. [Accessed: October-10-2018].
[9] V. Kathiresan, "Outlier Detection on Financial Card or Online Transaction Data Using Manhattan Distance Based Algorithm", International Journal of Computer Science & Technology in Contemporary Research, vol. 12, p. 1100 - 1103, 2016. Available:http://www.ijcrcst.com/papers/IJCRCST-DECEMBER16-01.pdf. [Accessed: October-10-2018].
[10] Y. Yazid, "Mendeteksi Kecurangan Pada Transaksi Kartu Kredit Untuk Verifikasi Transaksi Menggunakan Metode SVM", Indonesian Journal of Applied Informatics, vol. 1, no. 2, p. 61-66, 2017. Available: https://jurnal.uns.ac.id/ijai/article/view/14378. [Acces
sed: October-10-2018].
DOI: https://doi.org/10.22146/ijccs.46561
Article Metrics
Abstract views : 4710
| 
views : 3249
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1