Improving Financial Audits and Management of Compliance using Artificial Intelligence and Secure Cloud Technology

Modern financial ecosystem requires highly complex audit trails and more stringent compliance issues therefore require highly advanced secure and intelligent systems. This research outlines a hybrid framework which juxtaposes Artificial Intelligence (AI) and Secure Cloud Technology to improve financial audit process and establish strong compliance management. Taking advantage of the strengths of AI, the strengths in question including Natural Language Processing (NLP), anomaly detection and machine learning classifiers, this system is used to enhance data accuracy, and detect irregularities in real time and automate regulatory reporting. At the same time implementation of Zero Trust Cloud Architectures, along with homomorphic encryption, provides data integrity, privacy, and end to end security. The proposed methodology is centred around the integration of intelligent document processing and blockchain-verified logs in the federated learning framework - where both transparency and decentralization are fostered. In addition, predictive analytics are used for the prediction of possible risks and non-compliance incidents to facilitate proactive decision making. Extensive simulations are used to reveal enhanced performance relative to traditional systems, with increased accuracy of anomaly detections, audit traceability, and validation speed-up of compliance. This integration is not only focused on streamlining audit workflows, but can also cut on operational cost and human error as well. The results emphasize the importance of employing AI-enabled secure cloud infrastructures as a primary strategy for financial institutions in a growing regulated digital economy while trying to sustain compliance. The new system achieves a 96.2% rate of accuracy while auditing and only consumes 91.3% the time in compliance to encourage efficiency.