CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING
Abstract
Due to the rapid growth of the E-Commerce industry, the use of credit cards for online purchases has increased dramatically. In recent years, credit card fraud is becoming a major complication for banks as it has become very difficult for detecting fraud in the credit card system. To overcome this hardship Machine learning plays an eminent role in detecting the credit card fraud in the transactions. Modeling prior credit card transactions with data from ones that turned out to be fraudulent is part of the Card Fraud Detection Problem. In Machine learning the machine is trained at first to predict the output so, to predict the various bank transactions various machine learning algorithms are used. The SMOTE approach was employed to oversample the dataset because it was severely unbalanced. This paper the examines and overview the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest, XGBoost for credit card fraud detection. The assignment is implemented in Python and uses five distinct machine learning classification techniques. The performance of the algorithm is evaluated by accuracy score, confusion matrix, f1-score, precision and recall score and auc-roc curve as well.
References
[2] Credit Card Fraud Detection using Data science and Machine learning, S P Maniraj, Aditya Saini , Shadab Ahmed, Swarna Deep Sarkar, September 2019.
[3] A. Mishra, C. Ghorpade, “Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques” 2018 IEEE International Students' Conference on Electronics ,Electrical and Computer Science (SCEECS) pp. 1-5. IEEE.
[4] S. V. S. S. Lakshmi, S. D. Kavilla “Machine Learning For Credit Card Fraud Detection System”, unpublished [7] N. Malini, Dr. M. Pushpa, “Analysis on Credit Card Fraud Identification Techniques on the basis of KNN and Outlier Detection“, Advances in Electrical, Electronics, Information, Communication and BioInformatics (AEEICB), 2017 Third International Conference on pp. 255- 258. IEEE.
[5] C. Wang, Y. Wang, Z. Ye, L. Yan, W. Cai, S. Pan, “Credit card fraud detection on basis of whale algorithm optimized BP neural network”, 2018 13th International Conference on Computer Science & Education (ICCSE) pp. 1-4. IEEE.
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