Oversampling Techniques in Machine Learning Detection of Credit Card Fraud

More than ever before, the trend of doing things online has been explored and successfully implemented in many areas, including online shopping, online learning, working online, to name but a few.However, it has brought with it challenges, including the fraudulent use of credit cards in online purchases, the challenge of academic integrity in online learning, especially in doing exams online, and how to keep people in engaged in meetings, when working and studying online, and still give them adequate privacy.This paper deals with the attempt to detect the fraudulent use of credit cards in a timely manner, to avoid as much negative effects in the world of E-commerce and help maintain consumer confidence.Thus, in the current study, machine learning algorithm LightGBM has been used to detect fraudulent credit card transactions from a real-life dataset containing credit card transactions of the customers.The performance of this classifier is compared with two state-of-the-art classifiers -Decision Tree, and Random Forests, which are extensively used for solving such problems.Since there is data imbalance between fraudulent and nonfraudulent class, the data sampling technique used is the Synthetic Minority Oversampling Technique (SMOTE).SMOTE Oversampling performed best on all classifiers and LightGBM obtained precision value of 1 for both fraudulent and non-fraudulent class.