AI‐Driven Risk Governance for SMEs: From Predictive Analytics to Strategic Competitiveness

Small and medium-sized enterprises (SMEs) remain highly exposed to financial distress due to limited resources, volatile markets, and governance constraints. Traditional risk management often lacks a strategic and anticipatory orientation, highlighting the need for risk governance frameworks that integrate forecasting and adaptability. This study investigates how Artificial Intelligence (AI) supports SME risk governance through predictive analytics and explainable modeling. Using data from 10,000 Italian SMEs, four machine learning (ML) algorithms are compared, with XGBoost achieving the highest predictive accuracy. SHapley Additive exPlanations (SHAP) is applied to ensure interpretability and identify key financial and governance drivers of distress. Findings show that AI-based forecasting operates as an early warning system, improving crisis preparedness, transparency, and evidence-based decision-making. Beyond insolvency prevention, explainable AI (XAI) emerges as a strategic enabler of competitiveness, allowing SMEs to anticipate and adapt to technological, regulatory, and geopolitical disruptions. The study advances research on AI-driven risk governance by demonstrating its strategic relevance for competitive adaptation, while recognizing that challenges related to data dynamics and managerial interpretability remain critical frontiers for future inquiry.