Leveraging Machine Learning for Real-Time Fraud Detection in Digital Payments

Abstract

The rapid growth of digital payment systems has significantly transformed the way financial transactions are conducted, but it has also led to an increase in fraudulent activities. Real-time fraud detection is crucial in safeguarding both users and businesses from malicious activities. This paper explores the application of machine learning (ML) techniques to detect and prevent fraud in digital payment platforms. Machine learning algorithms, due to their ability to analyze large datasets and identify hidden patterns, offer an effective solution for detecting fraudulent transactions in real time.

Various ML approaches, including supervised learning, unsupervised learning, and ensemble methods, are evaluated for their efficiency in detecting suspicious activities such as identity theft, account takeover, and payment fraud. The study also highlights the importance of feature selection, data preprocessing, and model evaluation techniques to ensure high accuracy and minimal false positives in fraud detection. Algorithms such as decision trees, random forests, support vector machines, and neural networks are tested using transaction data, with a focus on the ability to adapt to evolving fraud patterns.

The paper further examines the challenges of real-time fraud detection, such as handling large volumes of transactions, managing data privacy, and dealing with adversarial attacks. It concludes that machine learning can significantly enhance the security of digital payment systems by providing scalable, adaptive, and timely fraud detection. Additionally, the paper suggests potential future research directions, including the integration of advanced deep learning techniques and the use of real-time analytics to improve detection rates and response times in digital payment environments.