Real-Time Fraud Detection in Digital Wallet Systems Using Advanced Machine Learning Algorithms

Abstract

With the rapid growth of digital payments and the increasing adoption of digital wallet systems, the risk of fraud has escalated significantly. Digital wallets have become a primary method for consumers to store and transfer funds, making them lucrative targets for cybercriminals. The complexity of transactions, coupled with the rise in digital fraud techniques, necessitates the development of robust systems capable of detecting fraudulent activities in real-time. This paper explores the application of advanced machine learning algorithms in real-time fraud detection within digital wallet systems. We delve into the types of fraud commonly encountered, the role of machine learning in identifying suspicious activities, and the challenges faced in building effective fraud detection models. Furthermore, we conduct experiments using various machine learning algorithms, evaluate their performance, and compare results to determine the most effective model for real-time fraud detection. Our findings indicate that machine learning, particularly techniques such as decision trees, neural networks, and ensemble learning, can significantly enhance the accuracy and speed of fraud detection in digital wallets, providing users with enhanced security and reducing financial losses.