Supervised Learning Methods for Identifying Credit Card Fraud
Ishaan Dawar, Narendra Kumar, Gursheen Kaur, Somya Chaturvedi, Aryan Bhardwaj, Meghavi Rana · 2023
Credit card fraud has increased due to the growing usage of credit cards for in-store and online transactions. These cards allow the user to make substantial purchases without carrying much money. They have simplified payment for clients and ushered in a new era of cashless commerce. Despite its many advantages, this kind of digital currency transaction is not without its own special risks. The incidence of credit card fraud increases proportionally with the growth of the user base. Therefore, it is essential to create models and solutions that prevent a user or community from falling victim and suffering financial and reputational losses. Typically, these methods are employed alone or in combination with ensemble or conceptual approaches to generate classifiers. This paper gives an overview of the fraud detection system using various supervised Machine Learning approaches i.e., Extreme Gradient Boosting (XGB), Decision Tree (DT), Logistic Regression (LR) and, Random Forest (RF), and compares their performance on factors like accuracy, sensitivity, etc. on a dataset for European cardholders obtained from Kaggle.