Transparency and Trust: Advancing Credit Card Fraud Detection with Explainable AI Models for Enhanced Compliance in the USA

Abstract

This paper explores the integration of Explainable Artificial Intelligence (XAI) models into credit card fraud detection systems to enhance transparency and trust in the USA's financial sector. As fraud detection becomes increasingly reliant on complex algorithms, there's a growing need for methods that not only identify fraudulent transactions but also provide understandable explanations for their decisions. We discuss the importance of transparency in regulatory compliance and consumer trust, highlighting how XAI techniques can bridge the gap between black-box algorithms and human comprehension. By elucidating the decision-making process behind fraud detection, XAI models can foster greater confidence among stakeholders while ensuring adherence to regulatory standards. We examine various XAI approaches and their applicability to fraud detection, emphasizing the significance of interpretability and accountability in algorithmic decision-making. Through this analysis, we advocate for the adoption of XAI models as a means to advance credit card fraud detection, promote transparency, and bolster compliance in the USA's financial landscape. This paper explores the pivotal role of Explainable AI (XAI) models in enhancing trust and compliance within the USA's credit card fraud detection landscape. By delving into the principles of transparency and interpretability, we elucidate how XAI models facilitate a deeper understanding of decision-making processes, thereby fostering trust among stakeholders. Through a comprehensive examination of AI-driven methodologies, coupled with regulatory frameworks, we demonstrate the potential of XAI to fortify compliance measures while combating fraudulent activities. This study advocates for the widespread adoption of XAI in credit card fraud detection systems, ultimately contributing to a more secure and resilient financial ecosystem in the USA.