Enhancing financial fraud detection with AI and cloud-based big data analytics: Security implications

The increasing sophistication of financial fraud necessitates the deployment of advanced technological frameworks that transcend traditional detection mechanisms. This paper investigates the integration of artificial intelligence (AI) with cloud-based big data analytics as a multifaceted approach to enhancing the efficacy of financial fraud detection systems. Leveraging machine learning algorithms, real-time data streaming, and high-performance distributed computing, the proposed paradigm offers scalable, adaptive, and context-aware fraud identification. Emphasis is placed on the architectural synthesis of AI-driven anomaly detection models with cloud-native infrastructures capable of ingesting, processing, and analyzing voluminous heterogeneous financial datasets. Furthermore, the study rigorously explores the security implications of cloud adoption, addressing vulnerabilities inherent in data transmission, access control, and algorithmic bias. Through a systematic evaluation of current methodologies and emerging practices, this research delineates a comprehensive framework that balances analytical performance with security resilience. The findings underscore the transformative potential of AI and big data convergence in redefining financial security paradigms and establishing proactive fraud mitigation strategies.