Abstract
Fraud detection and prevention remain critical challenges in the financial technology (fintech) industry. The rapid digitalization of financial services has increased the sophistication and frequency of fraudulent activities, necessitating robust and scalable solutions. This paper explores the transformative role of machine learning (ML) in enhancing fraud detection and prevention mechanisms. By leveraging supervised and unsupervised learning algorithms, including decision trees, support vector machines (SVMs), and deep learning models like convolutional neural networks (CNNs) and autoencoders, fintech companies can detect anomalies and mitigate fraudulent activities in real time. This study reviews state-of-the-art approaches, discusses real-world implementations, and evaluates the performance of ML models in fraud detection. Ethical considerations, including data privacy and algorithmic fairness, are also addressed. The findings highlight the potential of machine learning to revoluti