An Outlier Detection Approach on Credit Card Fraud Detection Using Machine Learning: A Comparative Analysis on Supervised and Unsupervised Learning

Abstract

Credit card fraud is a socially relevant problem that majorly faces a lot of ethical issues and poses a great threat to businesses all around the world. In order to detect fraudulent transactions made by the wrongdoer, machine learning algorithms are applied. The purpose of this paper is to identify the best-suited algorithm which accurately finds out fraud or outliers using supervised and unsupervised machine learning algorithms. The challenge lies in identifying and understanding them accurately. In this paper, an outlier detection approach is put forward to resolve this issue using supervised and unsupervised machine learning algorithms. The effectiveness of four different algorithms, namely local outlier factor, isolation forest, support vector machine, and logistic regression, is measured by obtaining scores of evaluation metrics such as accuracy, precision, recall score, F1-score, support, and confusion matrix along with three different averages such as micro, macro, and weighted averages. The implementation of local outlier factor provides an accuracy of 99.7 and isolation forest provides an accuracy of 99.6 under supervised learning. Similary in unsupervised learning, implementation of support vector machine provides an accuracy of 97.2 and logistic regression provides an accuracy of 99.8. Based on the experimental analysis, both the algorithms used in unsupervised machine learning acquire a high accuracy. An overall good, as well as a balanced performance, is achieved in the evaluation metrics scores of unsupervised learning. Hence, it is concluded that the implementation of unsupervised machine learning algorithms is relatively more suitable for practical applications of fraud and spam identification.