Abstract
Credit cards have become the part of human life these days. It facilitates users in various sectors. Intruders try to steal credit card information in many ways. Hence, security of sensitive information of credit cards is a major concern. We can apply different machine learning techniques to detect such frauds or anomalies. On basis of our survey, we found two outperforming classifiers of machine learning viz., Logistic Regression (LR) and Naïve Bayes (NB). This paper provides a method of fraud detection in credit card system using Random Forest (RF) classifier. The work is compared with the existing classifiers: LR and NB. Their performance is evaluated on various metrics viz., Accuracy, Precision, Recall, F1 Score and Specificity on some datasets of credit card system. It is observed that Random Forest is outperforming others. Random Forest gives 99.95% accuracy while accuracy of LR and NB is 91.16% and 89.35% respectively.
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Rai, A.K., Dwivedi, R.K. (2020). Fraud Detection in Credit Card Data Using Machine Learning Techniques. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_31
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DOI: https://doi.org/10.1007/978-981-15-6318-8_31
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