The Role of AI and Machine Learning in Strengthening Digital Wallet Security Against Fraud

Abstract

There are so many digital wallets, like PayPal, Google Pay, and Apple Pay, we have started transacting money in a completely different way. That said, with the growing popularity of digital wallets, fraud is increasing, which is becoming a mammoth task to secure users’ sensitive financial data. Rule-based systems, which are traditionally used for fraud detection, have failed to fight complex fraud schemes. In this work, we investigate the use of artificial intelligence (AI) and machine learning (ML) to enhance the security of digital wallets by studying their capacity to detect and prevent fraudulent transactions. Various AI/ML models were tested including Random Forests and Recurrent Neural Networks (RNNs), and results show that these models greatly increase fraud detection accuracy and decrease false positives. It compares AI/ML models with traditional fraud prevention techniques and shows that AI/ML models adapt to changing fraud patterns much more swiftly. Although AI/ML models give promising results, the implementation is not effective due to computational costs, false positives, and issues with data privacy. The research reveals that deploying AI/ML-based fraud detection systems within the business can enhance the security of digital wallets by a significant amount.