A COMPARATIVE STUDY OF LOGISTIC REGRESSION AND XGBOOST FO A COMPARATIVE STUDY OF LOGISTIC REGRESSION AND XGBOOST FOR CREDIT CARD FRAUD DETECTION R CREDIT CARD FRAUD DETECTION

Abstract

Since the dawn of recorded human history, fraud has entailed a variety of dishonest behaviors that vary greatly in their forms and tactics. Almost every purchase made today is done so online. Online transactions are completed using an easy-to-use, multi-party, straightforward approach that does not require the usage of cash. The annual loss resulting from fraudulent credit card transactions is in the billions. According to the 10th annual study on online fraud, between 2006 and 2008, 1.4% of online payments resulted in lost money; nevertheless, the real percentage of lost revenue increased as online sales increased. The annual loss resulting from fraudulent credit card transactions is in the billions. The 10th annual study on online fraud states that while 1.4% of online payments resulted in lost money between 2006 and 2008, the real percentage of lost revenue increased as online sales increased. The present dataset for this study was collected in September 2013 through credit card transactions made through Kaggle by cardholders throughout Europe. In this study, a model built with Logistic Regression techniques was compared to the XGBoost model that was based on standard evaluation criteria.