Credit card fraud detection using machine learning

• Author(s): Sushant Agrawal
• Paper ID: 1704006
• Page: 126-131
• Published Date: 16-01-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 7 January-2023

Abstract

For clients to avoid being charged for products they did not buy, credit card issuers must be able to recognise fraudulent credit card transactions. Data Science may be used to solve issues, and coupled with machine learning, its significance cannot be understated. With the use of credit card fraud detection, this research aims to demonstrate the modelling of a data set using machine learning. The Credit Card Fraud Detection Problem includes modelling prior credit card transactions using data from those that turned out to be fraudulent. This technique then determines the validity of a new transaction. The goal here is to minimise inaccurate fraud categories while detecting 100% of the fraudulent transactions. A classic example of categorization is the detection of credit card fraud. The analysis and pre-processing of data sets, as well as the use of several anomaly detection techniques to PCA-transformed Credit Card Transaction data, have been the main points of this approach.

Citations

IRE Journals:
Sushant Agrawal "Credit card fraud detection using machine learning" Iconic Research And Engineering Journals Volume 6 Issue 7 2023 Page 126-131

IEEE:
Sushant Agrawal "Credit card fraud detection using machine learning" Iconic Research And Engineering Journals, 6(7)