An Explainable AI–Enabled Risk-Adjusted Financing Framework for Investment Readiness in Fragile Agri-Food Systems

Agri-food enterprises operating in fragile settings—characterised by climate exposure, conflict, weak institutions, or acute market volatility—face interconnected financial, operational, and governance risks that constrain investment readiness and discourage capital providers. Conventional financing assessments often rely on static financial indicators, limited historical data, and standardised risk thresholds that may overestimate risk, overlook resilience, and inadequately reflect these enterprises' operating realities. This protocol paper outlines a proposed explainable artificial intelligence (XAI)-enabled risk-adjusted financing framework for assessing investment readiness and supporting transparent financing decisions in such contexts.

The framework will integrate enterprise-level financial and non-financial data—drawn from smallholder and SME agri-food enterprises in climate-vulnerable and institutionally weak regions—with contextual risk indicators to generate multidimensional risk profiles, investment-readiness scores, and differentiated financing recommendations. Supervised machine-learning models will estimate financing viability and risk, while SHapley Additive exPlanations (SHAP) will demonstrate how specific variables shape each assessment, addressing the tension between predictive performance and interpretability where opaque models risk reinforcing information asymmetries or excluding enterprises with incomplete records (Bussmann et al., 2020; Černevičienė and Kabašinskas, 2022).

A mixed-methods design combining enterprise and investment data, stakeholder interviews, and expert validation is proposed. Model performance will be assessed against predictive accuracy, calibration, robustness, and interpretability, alongside fairness criteria examining whether outputs disadvantage enterprises with sparse credit histories, such as women-led or smallholder ventures. Human oversight remains embedded throughout to contextualise outputs and prevent fully automated determinations.

This study contributes an integrated approach linking risk assessment, investment readiness, financing design, and explainable machine learning, intended to help investors distinguish perceived from empirically supported risk, and to improve capital allocation toward resilient ventures in fragile settings.