Machine Learning-Based Real-Time Fraud Detection in Financial Transactions

Abstract

To enhance the security and integrity of digital commerce, this study explores the application of real-time fraud detection in financial transactions through machine learning. The primary goals encompass investigating the efficacy of machine learning algorithms, pinpointing obstacles related to implementation, and proposing avenues for further research and improvement. The methodology entails a thorough analysis of the body of knowledge, including research studies and publications, emphasizing the concepts, procedures, and uses of machine learning algorithms in fraud detection. Key findings show that machine learning algorithms are effective at identifying fraudulent activity, but there are still issues with data collection, model interpretability, security risks, and regulatory compliance. The policy implications underscore the significance of stakeholder collaboration, accountability, and transparency in tackling new issues and building confidence in fraud detection systems. This study shows how machine learning-based methods may transform financial transaction fraud detection, opening the door to improved security and resilience in the digital economic ecosystem.