Machine Learning based Credit Card Fraud Detection
Y. Vijaya Lakshmi, Y. Sahithi Priyanka, Ala Harika, N. Rajitha, Dodda Bhargavi · 2023
Cashless transactions are increasing because electronic commerce technology is developing so quickly. Since everyone uses credit and ATM cards for transactions, fraud may also rise. The rise in fraud, which costs businesses money worldwide, the development of numerous procedures and strategies for spotting fraud, Analysis of user behaviors is necessary for fraud detection in order to identify users that act maliciously. Delinquent, fraudulent, intrusive, and account defaulting behaviors all fall under the umbrella of malicious behavior. This study analyzes credit card fraud detection by using machine learning algorithms, specifically the Random Forest and Decision Tree algorithm. The survey of current strategies utilized in credit card fraud detection were depicted. This study has employed the Principal Component Analysis (PCA) to perform feature selection and speed up the learning process. The comparison outcomes demonstrate that Random Forest (RF) outperforms Decision Tree (DT).