(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
Cloud computing and machine learning (ML) have revolutionized enterprise operations, enabling automation, scalability, and intelligent decision-making in financial web applications. Ensuring security in such frameworks is critical, given the sensitivity of financial data and the regulatory requirements governing transactions. This research presents a secure cloud-based architecture integrated with machine learning capabilities to automate customer interactions, financial forecasting, fraud detection, and personalized services in enterprise web applications. The framework leverages cloud infrastructure for elastic resource provisioning, secure storage, and distributed computation, while ML models analyze customer behavior, detect anomalies, and optimize workflows. Key security mechanisms, including end-to-end encryption, identity and access management, and anomaly-based intrusion detection, are incorporated to safeguard data integrity, confidentiality, and availability. The study evaluates performance metrics such as response time, prediction accuracy, scalability, and security effectiveness through simulation and prototype deployment. By combining secure cloud infrastructure with ML-driven automation, the framework aims to enhance operational efficiency, improve customer experience, and mitigate financial risks. The findings provide a roadmap for enterprises seeking to implement intelligent, secure, and scalable web applications while adhering to compliance standards and minimizing exposure to cyber threats.