AI-Driven Risk Scoring Engine for Financial Compliance in Multi-Cloud Environments

Abstract

The proliferation of multi-cloud architectures in financial institutions has amplified the complexity of adhering to global regulatory standards such as GDPR, SOX, and PCI-DSS. Traditional compliance frameworks, reliant on static rule-based systems, fail to address the dynamic risks inherent in distributed cloud environments. This paper proposes an AI-driven risk scoring engine that integrates machine learning (ML), real-time anomaly detection, and cross-platform data normalization to automate compliance monitoring. The engine employs ensemble learning and explainable AI (XAI) to prioritize risks while maintaining alignment with regulatory requirements. Evaluations demonstrate a 92% accuracy in risk prediction, outperforming legacy systems by 34%, with sub-second latency for large-scale transaction analysis.