Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Abstract

This research study aims to detect credit card frauds, such as accessibility of public data, high-class imbalance data, changes in fraud nature, and high rates of false alarm. Machine learning and deep learning algorithms have been used to detect frauds, but there is still a need to apply state-of-the-art deep learning algorithms to reduce fraud losses. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The European card benchmark dataset was used to evaluate the proposed model, which outperformed the state-of-the-art machine learning and deep learning algorithms.