AIoT-Driven Business Analytics for Financial Risk Management and Supply Chain Optimization: A Data-Driven Approach Using Predictive Modeling

Description

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 incorporates federated learning protocols to preserve data privacy across enterprise boundaries. These findings establish a rigorous empirical basis for deploying AIoT-driven analytics in high-stakes operational environments, contributing a replicable methodological template for researchers and practitioners at the intersection of intelligent systems, operations research, and risk governance.

Steps to reproduce

. We propose a novel, modular AIoT-AIBA architecture that formally delineates the data flow from heterogeneous IoT sources through hierarchical processing tiers to enterprise-level decision outputs, providing a replicable systems blueprint for practitioners. 2. We develop and empirically validate an ensemble predictive modeling strategy that combines LSTM-based temporal dependency extraction, XGBoost-based feature interaction modeling, and Transformer-based attention mechanisms for financial risk scoring and supply chain anomaly detection. 3. We introduce a federated learning protocol adaptation specifically designed for cross-organizational AIoT deployments, enabling collaborative model training without centralizing sensitive financial or operational data. 4. We present a comprehensive experimental evaluation on a multi-sector dataset of 148,000 records, benchmarking the AIBA framework against seven competing baselines across twelve performance metrics, establishing state-of-the-art results on both risk classification and supply chain optimization objectives.

Institutions

• North South University

  Dhaka Division, Dhaka

Categories

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