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

Akinniyi James Samuel *

Akin James LLC, Technology Director, Houston, Texas, United State.
 
Review
World Journal of Advanced Engineering Technology and Sciences, 2023, 09(02), 417-434.
Article DOI: 10.30574/wjaets.2023.9.2.0208
Publication history: 
Received on 08 June 2023; revised on 18 July 2023; accepted on 20 July 2023
 
Abstract: 
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. 
 
Keywords: 
Financial Fraud Detection; Artificial Intelligence; Cloud Computing; Machine Learning; Distributed Computing; Cybersecurity
 
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Copyright information: 
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0