Integration of artificial intelligence technologies into financial risk forecasting in the agricultural sector

Abstract

Financial risk is also a constant menace to the agricultural industry in Ukraine. Still, a key problem with conventional banking methods is that they cannot reflect the risk dynamics specific to the area. This paper questions the prevailing belief that artificial intelligence (AI) is universal, in the sense that it outperforms conventional econometric models in predicting credit interest rate volatility across 25 administrative regions (2015-2020). We find an empirical paradox: under the comparatively constant national level, the simple Linear Regression model performed more effectively than elaborate algorithms, with an accuracy rate of 82.35, which confirms the effectiveness of the principle of parsimony when measured against macroeconomic conditions. Nevertheless, the benefit of AI will be high in economically complex regions. Deep learning (ANN) and gradient boosting models identified non-linear risk patterns that linear models overlooked in agricultural centers such as Kherson and Dnipropetrovsk, further enhancing predictive performance by as much as 10.6 percentage points. These findings are consistent with the Adaptive Markets Hypothesis, which posits that the utility of technology depends on market volatility. Therefore, we suggest a precision banking model: a hybrid model in which stable areas would maintain linear efficiency, whereas shock-affected areas would use AI-powered risk detection to maintain the stability of agricultural credit in the post-war period.