Leveraging AI and Cloud Computing for Real-Time Fraud Detection in Financial Systems
Keywords:
AI, cloud computing, fraud detection, financial systems, machine learningAbstract
Traditional fraud detection systems in financial domains face significant challenges in processing vast amounts of transactional data in real time, often leading to delayed responses and undetected fraudulent activities. The integration of artificial intelligence (AI) and cloud computing offers a paradigm shift by enabling real-time fraud detection with adaptive, machine learning-driven approaches. Cloud-based AI systems leverage scalable computational resources to process high-velocity financial transactions while deploying deep learning models and anomaly detection techniques to identify fraudulent patterns with high accuracy. This paper explores the synergy of AI and cloud computing in fraud detection, detailing model architectures, real-time monitoring frameworks, and the impact of distributed computing on detection efficiency. Furthermore, it discusses implementation challenges, security concerns, and regulatory compliance issues, providing insights into optimizing fraud detection in modern financial infrastructures. The study concludes with future directions for enhancing fraud prevention methodologies through advanced AI and cloud innovations.
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M. Guo et al., "Quantum Algorithms for Anomaly Detection Using Amplitude Estimation," Phys. Rev. A, vol. 104, no. 5, pp. 052310, Nov. 2021.
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T. H. Pranto et al., "Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive-Based Approach," IEEE Access, vol. 10, pp. 123456–123470, 2022.
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