Review on Credit Card Fraud Detection using Machine Learning Algorithms
  IJCTT-book-cover
 		          
 
© 2020 by IJCTT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Pooja, Dr. Ashlesha
DOI :  10.14445/22312803/IJCTT-V68I6P104

How to Cite?

Pooja, Dr. Ashlesha , "Review on Credit Card Fraud Detection using Machine Learning Algorithms," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 22-27, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I6P104

Abstract
The advancement of new technologies and the fast growing of technological development have generated new possibilities as well as imposing new challenges. Fraud, the biggest challenges for business and organization, emerge with new technologies to take new and distinctive forms that are hidden and tougher to identify than the conventional forms of this crime. Credit card frauds also grow up along with growing in technology. It also noticed that financial fraud is extremely growing in the global communication improvement. It is being admitted every year that the loss because of this types of fraudulent activities is billions of dollars. These activities are performed so gracefully that it look similar to original transactions. Simply using of pattern matching technique and simple method really not useful for detecting these fraudulent activities. A well planned and systematic method has became need for all business and organization to minimizing chaos and carry out in place. Several technique has been evolved based on Artificial intelligence, Machine learning, Data mining, Genetic programming Fuzzy logic etc.. for detecting credit card fraudulent activities. Besides this technique, K-Nearest Neighbour algorithm and outlier detection methods are implemented to optimize the best solution for the fraud detection problem. These techniques proved to minimize the false alarm rates and increase the fraud detection rate.

Keywords
Credit Card, Fraud Detection, Machine Learning, Logistic Regression, K-Nearest Neighbour..

Reference
[1] N. Malini, Dr. M. Pushpa, “Analysis on Credit Card Fraud Identification Techniques basedon KNN and Outlier Detection“, Advances in Electrical, Electronics, Information, Communication and BioInformatics(AEEICB), 2017 Third International Conference on pp. 255258. IEEE.
[2] J. O. Awoyemi, A. O. Adentumbi, S. A. Oluwadare, “Credit card fraud detection using Machine Learning Techniques: A Comparative Analysis”, Computing Networking and Informatics (ICCNI), 2017 International Conference on pp. 1-9. IEEE.
[3] Z. Kazemi, H. Zarrabi, “Using deep networks for fraud detection in the credit card transactions”, Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on pp. 630-633. IEEE
[4] N. Kalaiselvi, S. Rajalakshmi, J. Padmavathi, “Credit card fraud detection using learning to rank approach”, 2018 Internat2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC) ional conference on computation of power, energy, Information and Communication (ICCPEIC) pp. 191196. IEEE
[5] Mrs. C. Navamani, M. Phil, S. Krishnan, “Credit Card Nearest Neighbor Based Outlier Detection Techniques”,International Journal of Computer Techniques -– Volume 5 Issue 2, Mar – Apr 2018
[6] F. Ghobadi, M. Rohani, “Cost Sensitive Modeling of Credit Card Fraud using Neural Network strategy”, 2016 Signal Processing and Intelligent Systems (ICSPIS), International Conference of pp. 1-5. IEEE
[7] Venkaata Ratnam Ganji,”credit card fraud detection using anti-K nearest neighbour algorithm ”,Internationnal journal on Computer Science and Engineering(IJCSE) VOL.4,06 june 2012,(1035-1039)
[8] Abhinav Srivastava, Amlan Kundu, Shamik Sural, and Arun K. Majumdar” Credit Card Fraud Detection Using Hidden Markov Model” VOL. 5, NO. 1, JANUARYMARCH 2008
[9] Divya.Iyer, Arti Mohanpurkar,Sneha Janardhan,Dhanashree Rathod,Amruta Sardeshmukh” credit card fraud detection using hidden markov model ” 978-14673- 01268/11/$26.00_c 2011 IEEE
[10] K.RamaKalyani, D.UmaDevi” Fraud Detection of Credit Card Payment System by Genetic Algorithm” Volume 3, Issue 7, July-2012
[11] Renu , Suman. "Analysis on Credit Card Fraud Detection Methods". International Journal of Computer Trends and Technology (IJCTT) 8(1):45-51, February 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
[12] Swapna K, and Babu M S P 2017 International Journal of Electrical & Computer Sciences IJECS-IJENS A Framework for Outlier Detection Using Improved Bisecting KMeans Clustering Algorithm. 17 0812
[13] Naik H 2018 International Journal for Research in Applied Science & Engineering Technology (IJRASET) Credit Card Fraud Detection for Online Banking Transactions 6 453457
[14] Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang and Xiangnan He / Generative Adversarial Active Learning for Unsupervised Outlier Detection (2019)
[15] Divya K T , Kumaran N S 2016 International Research Journal of Engineering and Technology (IRJET) Improved Outlier Detection Using Classic Knn Algorithm 3 892898
[16] Pooja Bhati, Manoj Sharma, “Credit Card Number Fraud Detection Using K-Means with Hidden Markov Method”, SSRG International Journal of Mobile Computing and Application, vol 2, iss 2, 2015.