Unified AI-Driven Data Intelligence for Cybersecurity Fraud Detection and Environmental Financial Risk Analysis
DOI:
https://doi.org/10.15662/IJARCST.2023.0605011Keywords:
AI-driven data intelligence, Cybersecurity, Fraud detection, Environmental financial risk, Machine learning, Risk analysis, Explainable AI, Federated learningAbstract
This paper presents a unified AI-driven data intelligence framework designed to enhance cybersecurity fraud detection and environmental financial risk analysis. By integrating advanced machine learning algorithms and data analytics, the framework offers comprehensive risk assessment and anomaly detection across diverse domains. In cybersecurity, the system identifies fraudulent activities in real-time, reducing false positives and improving threat response. For environmental financial risk, the model evaluates climate-related risks impacting financial portfolios, aiding in proactive decision-making. The unified approach enables seamless data integration, scalable processing, and improved interpretability, supporting regulatory compliance and operational efficiency. Future research directions include incorporating federated learning, explainable AI, and adaptive risk modeling to address evolving challenges in cybersecurity and environmental finance.References
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