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

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.

物联网人工智能（AIoT）与高级商业分析的融合，代表了各机构在金融风险管理与供应链运营优化方面的变革性范式转变。本文提出一套综合数据驱动框架，该框架整合边缘层传感器智能、云端预测分析与实时决策引擎，以解决金融敞口量化与物流网络效率优化领域的关键薄弱环节。该框架利用涵盖库存遥测数据、交易账本、物流GPS轨迹与市场波动率指数的异构物联网数据流，集成了包括长短期记忆网络（LSTM）、梯度提升机（GBM）、双向Transformer以及随机蒙特卡洛模拟在内的多种机器学习架构，以生成高保真风险预测与供应链优化方案。实验评估基于一份经精心筛选的多领域数据集展开，该数据集包含来自制造业、零售业与金融服务业的148000条带时间戳的记录。实验结果表明，集成AIoT的分析流程在违约风险分类任务中平均预测准确率达94.7%（受试者工作特征曲线下面积（AUC-ROC）=0.961）；相较于传统基于监控与数据采集（SCADA）系统的监测方案，该流程将供应链中断检测延迟降低了67.3%；在随机需求场景下，可使平均库存持有成本降低23.8%。该框架还集成了联邦学习协议，以实现跨企业边界的数据隐私保护。上述研究结果为在高风险运营环境中部署AIoT驱动的分析技术提供了坚实的实证基础，并为智能系统、运筹学与风险治理交叉领域的研究者与从业者提供了可复制的方法论模板。