A Review of Credit Card Fraud Detection Using Machine Learning

N.S. Shroff¹ , A.S. Vaishnav²

Section:Review Paper, Product Type: Journal
Vol.11 , Issue.2 , pp.1-6, Apr-2023

Online published on Apr 30, 2023

Copyright © N.S. Shroff, A.S. Vaishnav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Nowadays fraud has been increasing due to the establishment of online payment mode on different E-commerce platform.A credit card is a form of payment that lets you buy goods or services on credit from an issuer, usually a bank. You can make purchases up to a specified limit and then pay them off over time either in full or with minimum payments.There are several types of security features including fraud protection, verified by visa and master card secure code, address verification systems, and biometric authentication. Additionally, some cards offer the additional security feature of a chip and pin system which requires that the cardholder enter a secret code to make purchases.Still fraud has been executed using this card. In this fraud, banks, merchants, and organisations are losing billions of dollars. According to one survey, the prevalence of credit card fraud is rising by 12.5% a year. It is crucial to identify fraud using secure and effective methods. Nowadays, hybrid algorithms and artificial neural networks are used to detect fraud since they perform better than other methods. We will use dataset variables like "duration," "amount of transaction," and "V1 to V28" as derived parameters for this. We will build a model that will separate out fraudulent transactions from other transactions using machine learning techniques or algorithms.

Key-Words / Index Term :
Machine learning, Hybrid algorithms, Fraud, Fraudulent and Credit card

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