How can Artificial Intelligence Techniques Effectively Enhance Credit Card Fraud Detection Systems?

Abstract

The boom in Artificial Intelligence (AI) revolution is significantly transforming our everyday lives. Every aspect of technology, process, and system implements AI to provide a superior user experience, enhanced machine capabilities and advanced problem-solving and research capabilities. However, due to this technological revolution, rise in fraud has become an enormous challenge in the digital economy. This research paper aims to test different Machine Learning (ML) models to explore real-time fraud detection capabilities accurately, particularly for credit card fraud prevention systems. Technologies like online bank transfers and smartphone payments based on credit accounts are major contributors to fraudulent transactions. AI/ML models have proven to be industry-disruptors, robust, significantly faster and produce more accurate results. These advantages have led to launch of successful companies like OpenAI. This paper uses model metrics such as Supervised, Unsupervised, and Ensemble methods to improve the detection of unauthorized transactions for fraud detection. Models will be ranked for performance using accuracy metrics, F-1, and Area Under Curve (AUC) scores. While zero false rates are not yet achievable, this study aims to reach a reasonably low level by selecting an appropriate model.