An Intelligent Framework for Dynamic Credit Risk Management in Banking Using IoT-Driven Real-Time Data and Explainable AI

Traditional credit risk modeling, founded on the analysis of static and historical financial data, has been increasingly inadequate to handle the dynamics of modern, dynamic economies. The rear-view nature of the models fails to capture the real-time operational health of borrowers, a limitation that can lead to suboptimal lending decisions. The present study suggests a novel smart framework, the implementation of which is contended to revolutionize the management of credit risk through the integration of high-frequency data streams, originating from the Internet of Things (IoT), into the methods of Explainable Artificial Intelligence (XAI). The proposed architecture leverages real-time operational data—like supply chain logistics, equipment health, production levels, and inventory levels for corporate borrowers—to construct a dynamic, forward-looking risk profile that is constantly changing. The centerpiece of the framework is a hybrid deep learning model, wherein a Graph Neural Network (GNN) models complex inter-entity relationships within a supply chain, and a Long Short-Term Memory (LSTM) network for time-series sensor data analysis. Critically, an XAI layer using SHapley Additive exPlanations (SHAP) is included to enable full transparency and interpretability of model decisions, a requirement for regulatory compliance and stakeholder trust. The framework was evaluated against a synthetically generated yet realistic dataset combining financial records and simulated IoT data streams. The results demonstrate a significant improvement in predictive performance, observed via an Area Under the Curve (AUC) of 0.97, over traditional models that exclude real-time operational intelligence. The XAI module could provide transparent, feature-based, in addition to actionable explanations for risk score changes. This study argues that a convergence of IoT and XAI drives a paradigm shift from static, passive risk analysis to dynamic, proactive, and interpretable credit risk management, hence allowing financial institutions to make better-informed and timely lending decisions.

Keywords:

Credit Risk Management , Internet of Things (IoT) , Explainable AI (XAI) , Deep Learning , Graph Neural Network (GNN) , FinTech