AI-Powered Fraud Detection in Mobile Banking Applications: Leveraging Machine Learning for Enhanced Security

Abstract

This article explores the implementation of artificial intelligence and machine learning technologies in mobile banking fraud detection systems, addressing the growing need for sophisticated security measures in an increasingly digital financial landscape. The article examines various machine learning algorithms, including deep learning architectures, ensemble methods, and behavioral analytics, that enable real-time identification of fraudulent transactions while maintaining seamless user experiences. It discusses the technical challenges of implementing millisecond-level processing in distributed computing environments, the critical role of feature engineering in transforming raw transaction data into actionable insights, and the deployment strategies necessary for maintaining robust, scalable fraud detection systems. The integration of behavioral biometrics, device fingerprinting, and adaptive learning mechanisms creates multi-layered defense systems capable of evolving alongside emerging fraud tactics. Through analysis of MLOps frameworks and cloud-based architectures, the article demonstrates how modern financial institutions can achieve continuous model improvement while ensuring regulatory compliance and system reliability. The article indicates that successful fraud detection in mobile banking requires a comprehensive approach that balances security effectiveness with user experience, leveraging advanced technologies to protect financial assets while maintaining customer trust in digital banking services.