Quest Journals Journal of Research in Humanities and Social Science Volume 8 ~ Issue 2 (2020)pp.: 04-11 ISSN(Online):2321-9467 www.questjournals.org Corresponding Author:Andhavarapu Bhanusri4 | Page Research Paper Credit card fraud detection using Machine learning algorithms Andhavarapu Bhanusri (Assistant professor, Department of Information Technology , ANITS , Sangivalasa ,Visakhapatnam) K.Ratna Sree Valli , P.Jyothi , G.Varun Sai , R.Rohith Sai Subash (B.Tech , Department of Information Technology , ANITS , Sangivalasa ,Visakhapatnam) Corresponding Author: Andhavarapu Bhanusri ABSTRACT:Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. Since credit card is the most popular mode of payment, the number of fraud cases associated with it is also rising.Thus, in order to stop these frauds we need a powerful fraud detection system that detects it in an accurate manner. In this paper we have explained the concept of frauds related to credit cards.Here we implement different machine learning algorithms on an imbalanced dataset such as logistic regression, naïvebayes,random forest with ensemble classifiers using boosting technique. An extensive review is done on the existing and proposed models for credit card fraud detection and has done a comparative study on these techniques. So Different classification models are applied to the data and the model performance is evaluated on the basis of quantitative measurements such as accuracy, precision, recall, f1 score, support, confusion matrix. The conclusion of our study explains the best classifier by training and testing using supervised techniques that provides better solution. KEYWORDS:Accuracy, f1 score, precision, recall, support, fraud detection, supervised techniques, credit card Received 04 Mar.,2020; Accepted 22Mar.., 2020 © The author(s) 2020. Published with open access at www.questjournals.org I. INTRODUCTION In recent years, the prevailing data mining concerns people with credit card fraud detection model based on data mining. Since our problem is approached as a classification problem, classical data mining algorithms are not directly applicable.This project is to propose a credit card fraud detection system using supervisedlearning algorithm. supervised algorithms are evolutionary algorithms which aim at obtaining better solutions as time progresses.Credit card is the most popular mode of payment. As the number of credit card users is rising world-wide, the identity theft is increased, and frauds are also increasing.In the virtual card purchase, only the card information is required such as card number, expiration date, secure code, etc. Such purchases are normally done on the Internet or over telephone. To commit fraud in these types of purchases, a person simply needs to know the card details. The mode of payment for online purchase is mostly done by credit card. The details of credit card should be kept private. To secure credit card privacy, the details should not be leaked. Different ways to steal credit card details are phishing websites, steal/lost credit cards, counterfeit credit cards, theft of card details, intercepted cards etc. For security purpose, the above things should be avoided. In online fraud, the transaction is made remotely and only the card’s details are needed. A manual signature, a PIN or a card imprint are not required at the purchase time. In most of the cases the genuine cardholder is not aware that someone else has seen or stolen his/her card information. The simple way to detect this type of fraud is to analyze the spending patterns on every card and to figure out any variation to the “usual” spending patterns. Fraud detection by analyzing the existing data purchase of cardholder is the best way to reduce the rate of successful credit card frauds. As the data sets are not available and also the results are not disclosed to the public. The fraud cases should be detected from the available data sets known as the logged data and user behavior. At present, fraud detection has been implemented by a number of methods such as data mining, statistics, and artificial intelligence. 1.1 Types of Algorithms