Network-Aware FinTech Intelligence for ESG Risk Forecasting: A Graph Neural Network and Transformer-Based NLP Approach

Environmental, Social, and Governance (ESG) risks increasingly propagate across interconnected supply chains, yet conventional ESG assessment methods remain largely reliant on firm-level disclosures and static ESG ratings that often overlook indirect risk transmission among trading partners. This study develops a network-aware artificial intelligence (AI) framework for forecasting ESG risk by integrating Graph Neural Networks (GNNs), transformer-based natural language processing (NLP), explainable AI, and conventional machine-learning techniques. The proposed framework combines supply-chain network structures, shipment-level trade information, ESG controversy records, governance indicators, and transformer-derived ESG sentiment extracted using FinBERT and RoBERTa. Using a dataset of 11,386 firms across 27 industries from 2015 to 2025, the proposed GNN achieved the highest predictive performance, outperforming conventional machine-learning models with an ROC-AUC of 0.913. The results further demonstrate that supply-chain network centrality and transformer-derived ESG sentiment substantially improve the early identification of firms exposed to future ESG controversies. By integrating network relationships with textual ESG intelligence, the proposed framework advances FinTech-enabled ESG analytics and provides a scalable approach for proactive risk monitoring, sustainable investment decision-making, and supply-chain risk management.