Credit Card Fraud Detection Using Machine Learning: A Review

Abstract

Abstract: Emerging technology has brought about transformative changes across various vital industries such as healthcare, finance, manufacturing, transportation, and e-commerce. Among these sectors, the financial industry, in particular, has experienced significant positive shifts due to technological advancements. Banking services have embraced digitization and the evolution of e-commerce, resulting in a notable growth in the utilization of credit cards. However, with these advancements comes a challenge: an increase in fraudulent activities. This challenge has prompted the financial industry to take proactive measures to ensure the security and effectiveness of its operations. The surge in credit card usage has unfortunately attracted a higher number of fraudulent actors, leading to several predicaments for the banking sector. Amidst these challenges, financial institutions are committed to safeguarding credit card transactions and providing secure e-banking services to their clients. To address this, they are actively engaged in the development of more sophisticated fraud detection techniques. These techniques aim to not only identify a broader range of fraudulent transactions but also to enhance the overall effectiveness of fraud prevention systems and fraud detection systems. This article aims to provide a comprehensive overview of the key elements that constitute effective fraud detection, along with a deep dive into the current systems and methodologies in place. By shedding light on the prevalent challenges and complexities associated with fraudulent activities in the banking industry, the article also underscores the vital role that machine learning techniques play in the realm of solutions. In essence, this article seeks to showcase the positive trajectory of the financial industry's response to emerging challenges, highlighting its commitment to leveraging cutting-edge technology to ensure secure and seamless banking experiences for all clients