Machine learning algorithms to prevent fraudulent transactions in real-time

Abstract

In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector