Abstract

The financial and trade sectors are experiencing unprecedented growth in digital transactions, cross-border operations, and high-frequency trading, creating critical challenges in risk management, fraud detection, and operational safety. Conventional systems are increasingly inadequate to address complex, evolving threats, necessitating the adoption of advanced AI and machine learning frameworks. This study proposes cloud-native risk-aware AI and machine learning models for banking operations, trade safety, and 5G-enabled web services. The framework integrates predictive machine learning for anomaly detection, generative AI for simulating rare high-risk scenarios, and risk-aware scoring mechanisms to prioritize critical events. Cloud-native deployment ensures elastic scalability, fault tolerance, and low-latency processing, while 5G connectivity facilitates real-time analytics across geographically distributed systems. Secure ETL pipelines and privacy-preserving techniques, including differential privacy and secure multi-party computation (SMPC), ensure compliance with regulatory standards such as GDPR and PCI DSS. Experimental evaluation on synthetic and real-world datasets demonstrates detection accuracy exceeding 95%, significant reductions in false positives, and improved operational efficiency. The proposed framework provides a comprehensive, adaptive, and secure solution for modern banking and trade environments, enabling real-time decision-making, enhanced risk mitigation, and privacy-preserving analytics in web-based services