Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security

Abstract

Financial fraud poses a significant threat to the global economy, necessitating advanced measures for detection and prevention. This paper explores the application of machine learning techniques to enhance transaction security and combat financial fraud. It provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, neural networks, and anomaly detection. Each technique's application in identifying and preventing fraudulent activities is discussed, along with their advantages and limitations. Challenges in implementing machine learning for fraud detection, such as data quality, scalability, real-time processing, and model interpretability, are examined. Ethical and privacy concerns associated with using machine learning in financial transactions are also addressed. By highlighting these aspects, the paper aims to contribute to developing more effective and ethical machine learning-based fraud detection systems, ensuring robust transaction security and fostering trust in financial institutions.