Scalable Cloud-Based Machine Learning for Fraud and Network Threat Intelligence in Financial Markets Leveraging Healthcare Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806815Keywords:
Scalable Machine Learning, Cloud Computing, Fraud Detection, Network Threat Intelligence, Financial Markets, Healthcare Analytics, Anomaly Detection, Predictive Modeling, Cybersecurity, Big DataAbstract
The rapid digitization of financial markets has amplified the risks of network intrusions and fraudulent activities, necessitating advanced analytics for proactive threat detection. This research presents a scalable, cloud-based machine learning framework that leverages methodologies from healthcare analytics to enhance fraud and network threat intelligence in financial systems. By adapting predictive modeling, anomaly detection, and risk assessment techniques commonly used in healthcare data analysis, the proposed system can identify suspicious transactions and network anomalies in real time. The cloud infrastructure enables high-throughput processing of large-scale financial data while ensuring scalability and resilience. Experimental evaluations on simulated and real-world financial datasets demonstrate significant improvements in detection accuracy, reduced false-positive rates, and faster response times compared to traditional methods. The integration of healthcare analytics principles provides a novel perspective for modeling risk and identifying complex patterns, establishing a robust approach to secure financial markets against evolving cyber threats.References
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