AIoT-Driven Business Analytics for Financial Risk Management and Supply Chain Optimization: A Data-Driven Approach Using Predictive Modeling
Abstract
The integration of Artificial Intelligence of Things (AIoT) with advanced business analytics represents a transformative paradigm shift in how organizations manage financial risk and optimize supply chain operations. This paper presents a comprehensive data-driven framework that unifies edge-layer sensor intelligence, cloud-based predictive analytics, and real-time decision engines to address critical vulnerabilities in financial exposure quantification and logistics network efficiency. Leveraging heterogeneous IoT data streams—spanning inventory telemetry, transactional ledgers, logistics GPS traces, and market volatility indices—the proposed framework employs an ensemble of machine learning architectures including Long Short-Term Memory (LSTM) networks, Gradient Boosting Machines (GBM), Bidirectional Transformers, and stochastic Monte Carlo simulation to generate high-fidelity risk forecasts and supply chain prescriptions. Experimental evaluation was conducted on a curated multi-domain dataset comprising 148,000 timestamped records drawn from manufacturing, retail, and financial services sectors. Results demonstrate that the AIoT-integrated pipeline achieves a mean prediction accuracy of 94.7% for default risk classification (AUC-ROC = 0.961), reduces supply chain disruption detection latency by 67.3% compared to conventional SCADA-based monitoring, and yields a 23.8% reduction in average inventory holding cost under stochastic demand scenarios. The framework further…
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