Cloud-Integrated AIoT Framework for Real-Time Credit Risk and Supply Chain Analytics: A Data generated Conceptualization based on cloud & Financial Technologies.

This paper presents a cloud-integrated Artificial Intelligence of Things (AIoT) framework for real-time credit risk and supply chain analytics. The proposed system combines IoT-driven data streams with scalable cloud infrastructure to enable continuous monitoring and predictive modeling of financial and operational risks. Machine learning models, including Probability of Default (PD) and Loss Given Default (LGD), are deployed within a real-time analytics pipeline to assess dynamic risk exposure across interconnected systems. The framework supports decision intelligence through automated insights and anomaly detection. Experimental evaluation using synthetic datasets demonstrates improved predictive performance and reduced latency compared to traditional batch-processing approaches. SHAP-based explainability analysis provides transparent, regulator-compliant risk predictions achieving AUC of 0.86 and R-squared of 0.99 with gradient boosting. The results highlight the effectiveness of integrating AIoT and cloud computing for scalable, real-time business analytics in fintech and supply chain domains.