Credit Card Fraud Detection from Imbalanced Dataset Using Machine Learning Algorithm
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Swati Warghade, Shubhada Desai, Vijay Patil|
|DOI : 10.14445/22312803/IJCTT-V68I3P105|
How to Cite?
Swati Warghade, Shubhada Desai, Vijay Patil, "Credit Card Fraud Detection from Imbalanced Dataset Using Machine Learning Algorithm," International Journal of Computer Trends and Technology, vol. 68, no. 3, pp. 22-28, 2020. Crossref, 10.14445/22312803/IJCTT-V68I3P105
In Today’s world, credit card is the most accepted payment mode for both online as well as offline, it provide cashless shopping at every shopping mall. It is the most convenient way to do online transaction. Therefore, risk of fraud credit card transaction has also been increasing. With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection algorithm has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. This paper aims at analysing various machine learning techniques using various metrics for judging various classifiers. This model aims at improving fraud detection rather than misclassifying a genuine transaction as fraud.
Credit Card Fraud Detection, Imbalanced dataset, SMOTE.
 Ibtissam Benchaji, Samira Douzi , Bouabid El Ouahidi . “Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for credit card fraud detection ?, 2nd Cyber Security in Networking Conference (CSNet) 2018.
 Sahil Dhankhad, Emad Mohammed, Behrouz Far. “Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: Comparative Study”, IEEE 2018.
 Ankit Mishra, Chaitanya Ghorpade. “Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques“, IEEE International Students? Conference on Electrical, Electronics and Computer Science 2018.
 Yasmirah Mandasari Saragih, Andysah Putera Utama Siahaan, “Cyber Crime Prevention Strategy in Indonesia”, SSRG International Journal of Humanities and Social Science volume 3 Issue 6 November to December 2016
 Chunzhi Wang, Yichao Wang, Zhiwei Ye, Lingyu Yan, Wencheng Cai, Shang Pan. “Credit card fraud detection based on whale algorithm optimize HG BP neural network” , The 13th International Conference on Computer Science & Education. Colombo, Sri Lanka, (ICCSE 2018)
 Shiyang Xuan, Guanjun Liu, Zhenchuan Li, Lutao Zheng, Shuo Wang, Changjun Jiang. “Random Forest for Credit Card Fraud Detection”,978–1–5386–5053–0/18/$31.00© IEE 2018.
 Haibing Li, Wing-Lun Lam, Chi-Wai Chung, Man-Leung Wong, “Financial Fraud Detection: Multi-Objective Genetic Programming with Grammars and Statistical Selection Learning”, SSRG International Journal of Computer Science and Engineering Volume 7 Issue 2 – February 2020.
 https://www.geeksforgeeks.org/ml-credit-card-fraud detection/