AI-Powered Supplier Risk Intelligence: Predicting Financial and Geopolitical Supply Chain Disruptions in U.S. Critical Industries

Abstract

In today's environment of increasingly costly and geopolitically volatile global supply chains, it is essential to have predictive risk intelligence across the critical industries in the United States. The research proposes an AI supplier risk intelligence framework combining financial distress prediction, using XGBoost, and geopolitical analysis, utilizing BERT. The framework compared 10,245 suppliers from 6 years of financial data (2018-2024) and 15 million geopolitical text documents. Results show that the model is 89% accurate in financial distress prediction (AUC-ROC = 0.89, F1 = 0.85), 19.1× more likely to predict disruption for suppliers with both financial and geopolitical risks (F1 = 0.82, AUC-ROC = 0.88), and has a 3.4-month average lead time for warning of financial distress, which is ahead of the 3-month target. The single-dimension baselines gave an average accuracy of 67% while the unified multi-modal framework achieved 87%. There were economic impact validations, with $480 million prevented from loss (semiconductors), $12 million savings (pharmaceuticals), and $2.45 billion avoided (energy). This holistic approach allows for proactive supplier diversification, inventory buffering, and contract renegotiations, fostering resilience for U.S. supply chains in the face of growing uncertainties around the world.

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