Feature Selection and Optimization of Classifiers for Detection of Credit Card Frauds

Abstract

Listen

Translate article

Nowadays, in the digital era, the use of credit cards is widespread. People buy and sell goods from home. E-commerce brings a boom to the lifestyle of the people, for the working family, it is a preferable tool to purchase and pay through online mode in order to save the time and visiting hours. However, with the increasing use of credit cards, there have also been a lot of cases of credit card frauds that augmented much in the corona period. Fake credit card transactions significantly affect financial institutions, banks, and clients, whereas fraudsters try to develop new ways of committing fraud daily. To avoid credit card fraud and maintain the credibility of our clients, we need to build a model for credit card detection. There are the various machine learning tools that can be applied in detection of such frauds, these tools can be used to design an enhanced model to detect credit card frauds efficiently. In this view, the study presents a feature selection technique approach of machine learning with different classifiers. The study proposed an optimization algorithm using ‘Firefly’ for feature selection with four different classifiers: neural network, k-nearest neighbors, Support Vector Machine, and decision tree. The performance of all these four classifiers is compared on the basis of accuracy parameter.