AI-Augmented Financial De-Risking Framework for Climate-Resilient Agriculture in Nigeria: A Machine Learning and Monte Carlo Simulation Approach

Abstract

Climate-induced yield volatility creates significant financial risks for smallholder farmers and agricultural lenders in Nigeria. Despite advances in atmospheric science and artificial intelligence, there appears to be no widely adopted framework that translates climate data into actionable financial risk metrics for agricultural lending in the Nigerian context. This study develops and validates an AI-augmented framework for predicting crop yields and quantifying financial default risk using secondary climate and agricultural data from Ondo State (2008–2025). Secondary historical climate data were obtained from the Nigerian Meteorological Agency (NiMet) and supplemented with ERA5 reanalysis data. Secondary crop yield data for oil palm and maize were obtained from the Ondo State Ministry of Agriculture. Three machine learning algorithms—Random Forest, XGBoost, and Artificial Neural Networks—were trained and validated using 10-fold cross-validation with Bayesian hyperparameter optimization. The selected yield prediction model was embedded within a Monte Carlo simulation framework (10,000 iterations) to generate Probability of Default (PD) scores under three climate scenarios. The XGBoost algorithm achieved the highest predictive accuracy for both crops (oil palm: R² = 0.86, RMSE = 1.09 tons/ha; maize: R² = 0.84, RMSE = 0.31 tons/ha). Feature importance analysis identified cumulative rainfall during flowering (importance = 0.34) and growing degree days during vegetative growth (importance = 0.29) as the dominant predictors. PD ranged from 12.3% under favourable conditions to 67.2% under drought scenarios. These findings suggest that AI-augmented risk assessment using secondary data may provide credible, forward-looking financial metrics for agricultural lending in data-sparse environments. The framework offers a replicable model for climate-resilient agricultural finance across Nigeria.