Advanced Temporal Anomaly Detection with AI in Blockchain-Powered FinTech Projects
Keywords:
AI-driven, temporal anomaly detection, blockchain technology, machine learningAbstract
Frequent use of blockchain technology in Fintech raised transaction security and integrity concerns. To detect the temporary anomalies in blockchain-powered FinTech applications is crucial for identifying fraud and security breaches. Improving anomaly detection with the help of AI can solve this concern, as AI can automatically identify transaction patterns and detect suspicious or malicious activities using machine learning and deep learning algorithms. This article focuses on blockchain based fintech system, with AI based anomaly detection models which increases the real time transaction monitoring, fraud detection and system security.
Downloads
References
Smith, A., & Thompson, B. (2022). Machine learning models for anomaly detection in financial systems. Journal of Financial Technology, 18(4), 67-79.
Patel, K., & Roberts, L. (2023). Unsupervised anomaly detection in blockchain data. Blockchain and Security Review, 9(2), 112-123.
Zhang, Q., & Li, Y. (2021). Deep learning for temporal anomaly detection in blockchain applications. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 45-58.
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Chen, Z., & Lee, M. (2020). Detecting double-spending attacks with temporal anomaly detection models. Cybersecurity Research Journal, 15(1), 29-40.
Kumar, R., & Patel, V. (2021). AI-powered detection of market manipulation in decentralized exchanges. Journal of Blockchain Security, 7(3), 88-102.
Singh, D., & Gupta, R. (2023). Automated fraud detection using AI in blockchain. Journal of Artificial Intelligence in Finance, 6(4), 112-125.
Wang, H., & Zhang, J. (2020). Detecting money laundering using machine learning models in blockchain applications. FinTech Innovations, 4(2), 55-66.
Li, X., & Xu, H. (2022). Data challenges in machine learning for blockchain applications. Journal of Data Science in Blockchain, 3(1), 12-24.
Zhang, L., & Wei, X. (2023). Federated learning in blockchain for privacy-preserving AI models. Journal of Blockchain Technology, 14(3), 101-113.
Williams, T., & Robinson, M. (2024). Future directions for hybrid AI models in anomaly detection. Artificial Intelligence in Financial Systems, 10(2), 99-110.
