CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Abstract

A bank or financial services provider issues credit cards, which enable its holders to borrow money to pay for products and services from businesses that accept credit cards. Credit card firms must be able to recognize fraudulent credit card transactions in order to prevent customers from being charged for products they did not purchase. With everything being done online these days, there is a risk of card abuse and potential financial loss. By using machine learning approaches, data science may tackle challenges of this kind. It deals with credit card fraud detection and machine learning modeling of the dataset. Data is the primary component of machine learning, thus modeling previous credit card transactions with the data of those that turned out to be fraudulent is important. Subsequently, the constructed model is employed to identify the fraudulentness of a novel transaction. Sorting out whether or not there was fraud is the goal. Prior to applying a machine learning algorithm to the credit card dataset and determining the parameters and performance measures, the initial phase entails data analysis and pre-processing.