Enhancing Fraud Detection in Credit Card Transactions using Diverse Machine Learning Techniques

Abstract: Regular online card exchanges have expanded as a result of innovative headways in ranges like e-commerce and monetary innovation (FinTech) applications. Credit card extortion has expanded as a result, having an affect on card backers, retailers, and as well as banks. In this manner, making frameworks to ensure the astuteness and security of credit card exchanges is pivotal. In this ponder, we utilize skewed real-world datasets from European credit cardholders to build a machine learning (ML) based system for credit card extortion discovery. We re-sampled the dataset utilizing the Synthetic Minority over- sampling method (SMOTE) in arrange to address the issue of lesson lopsidedness. We evaluated this system with the taking after machine learning methods: Extreme Gradient Boosting (XGBoost).

Keywords: SMOTE, credit card, data resampling, fraud detection, XGBoost, machine learning.