Secure and Scalable Financial Intelligence Systems Using Big Data Analytics in Hybrid Cloud Environments

Abstract

The article looks into the development of scalable and safe finance intelligence infrastructure in the hybrid cloud environment, which is grounded on the big data analytics. The degree of security and scalability when entering a large volume of data is highly important in the modern world where the financial sphere highly depends on digital technologies. To manage these issues, the framework proposed in this study will be the integration of big data analytics with cloud computing that would provide a comprehensive degree of data protection, real-time processing opportunities, and scalability. The proposed system will enhance the data security and processing power because the hybrid cloud environments provide the flexibility of the public cloud and the control and privacy of the private clouds. The system will also include an architecture of the elements of data storage, data processing, and analytics that will be backed up by the secure encryption protocols and access control measures to keep sensitive financial information under lock and key. This framework also makes scalability straightforward because the system is able to support increased loads of data without compromising its performance as well as its security. The paper also has the details of integrating AI and machine learning into predictive analytics and can be applied in making real-time decisions and in evaluating risks. The article highlights the most important aspect of regulatory compliance, data privacy laws and use of advanced encryption software to secure financial information on the cloud. It is a supplement to the field because it gives an all-encompassing model that financial institutions that desire to progress their analytical framework might employ without losing their level of security and compliances.