Leveraging AI and Cloud Computing for Real-Time Fraud Detection in Financial Systems
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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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References
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A. Anwar, M. Ahmed, and S. Khan, "Blockchain-Based Anomaly Detection for Secure Industrial IoT Applications," IEEE Access, vol. 11, pp. 12345–12356, 2023.
L. Hernandez Aros et al., "Financial Fraud Detection Through the Application of Machine Learning Techniques: A Literature Review," Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–12, 2023.
M. Guo et al., "Quantum Algorithms for Anomaly Detection Using Amplitude Estimation," Phys. Rev. A, vol. 104, no. 5, pp. 052310, Nov. 2021.
A. Anwar, M. Ahmed, and S. Khan, "Blockchain-Based Fraud Prevention in Industrial IoT," IEEE Access, vol. 12, pp. 23423–23434, 2023.
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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G. S. Nadella et al., "Blockchain Fraud Detection Using Unsupervised Learning," in Proc. 2024 Int. Conf. Comput. Commun. Control (IC3),
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.