ARTIFICIAL INTELLIGENCE ADVANTAGES IN CLOUD FINTECH APPLICATION SECURITY
Abstract
The convergence of Artificial Intelligence (AI) and cloud technology has revolutionized various industries, including finance and technology (fintech). In the fintech sector, the integration of AI with cloud computing has led to significant improvements in application security. This paper explores the advantages that Artificial Intelligence brings to cloud-based fintech application security. It delves into the various AI-powered mechanisms that enhance security, such as anomaly detection, fraud prevention, threat intelligence, and risk assessment. Additionally, this paper addresses the challenges and potential risks associated with the use of AI in cloud fintech application security. By analyzing case studies and real-world examples, this paper demonstrates the tangible benefits of AI in safeguarding sensitive financial data and ensuring the integrity of fintech applications.
References
Li, Y., & Fu, X. (2018). Anomaly detection and prediction in cloud computing for financial applications in the 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 426-433).
Deka, B., & Borah, S. S. (2020). Anomaly detection in the cloud using machine learning techniques: A review Journal of King Saud University-Computer and Information Sciences
Deng, Y., Huang, J., & Jin, H. (2020). A survey of deep learning for fraud detection in financial services Big Data Mining and Analytics, 3(3), 216-227
Fournier-Viger, P., Gomariz, A., Gueniche, T., & Soltani, A. (2020). A survey of the state-of-the-art of common algorithms and methods in fraud detection arXiv preprint arXiv:2009.02929.
Liao, Y., & Hsueh, C. F. (2018). Enhancing financial risk prediction with topic modeling using news Decision Support Systems, 112, 81–89.
Ma, L., Sun, Y., & Li, H. (2019). A survey of machine learning for big data processing EURASIP Journal on Advances in Signal Processing, 2019(1), 1–18.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems O'Reilly Media.
Christmann, A., & Steinwart, I. (2010). Consistency and robustness of kernel-based regression in convex risk minimization. The Annals of Statistics, 38(6), 2991–3026
Yoo, S., & Lee, H. (2020). Adversarial Attacks on Deep Learning Models in the Fintech Domain In Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (pp. 315–341). Springer, Cham.
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., ... & Madry, A. (2019). On evaluating adversarial robustness arXiv preprint arXiv:1902.06705.
