期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：7
页码：3101-3106
DOI：10.35629/5252-030729232930
语种：English
出版社：IJAEM JOURNAL
摘要：The project titled “Comparative analysis and prediction of credit card transactions detects the fraudulent card during transactions and alerts the customer regarding the fraud. This project also aims in minimizing the number of false alerts. Here we implement different machine learning algorithm on an imbalanced dataset such as Light gradient classifier, XGB Classifiers. Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons - first, the profiles of normal and fraudulent behaviour change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection techniques used . This paper investigates the performance of Support vector classifier, Decision tree classifier, Random forest ,xgboost , LightGreadient, k-nearest neighbor and logistic regression on highly skewed credit card fraud data.
