Implementation of Credit Card Fraud Detection

Abstract

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable finacial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse machine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demonstrate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.