Revolutionizing Credit Card Fraud Detection: Harnessing Machine Learning and Data Science for Enhanced Security

Abstract

Credit cards allow you to make purchases, debt transfers, and cash advances but require you to repay the loan. Credit cards have been important over the last few decades because they allow us to collect incentives without changing our spending habits. It also simplifies spending tracking. Credit cards protect against fraud better than debit and are safer than cash. Responsible credit card use is a fast and easy approach to improve credit. Diners Club was the first contemporary credit card, founded in 1950. Cheap products and services can be purchased using a credit card. However, credit card fraud is common due to lost or stolen credit cards, skimming your credit card at a gas station pump, hacking your computer, calling about fake prizes or wire transfers, phishing attempts, looking over your shoulder at checkout and stealing your mail, etc. With unsupervised machine learning methods, we will use unlabeled data in this project to find patterns and dependencies in the credit card fraud detection dataset, allowing us to group data samples by similarities without manual labelling and detect frauds.