Аннотация
In the context of increasing economic uncertainty, climate variability, and rising credit exposure in Kazakhstan’s key industries, the ability to accurately forecast corporate financial risks has become critically important. This article examines the practical application of artificial intelligence (AI) in improving financial risk prediction, with a focus on Kazakhstan’s agricultural and leasing sectors. The study evaluates how user-friendly AI tools—such as rule-based expert systems, satellite imagery analytics, automated reporting modules, and AI-powered inventory platforms—can support early warning systems and enhance operational decision-making. Scientific relevance is ensured through the analysis of real-life cases, including Farmonaut’s crop monitoring system and KazAgroFinance’s AI-driven asset verification tool. These technologies enable more accurate forecasts of yields and drought risks, improve resource planning, reduce manual errors, and strengthen loan oversight. The methodology is based on comparative analysis of financial and operational indicators over a 10-year period (2015–2024), with a focus on pre- and post-AI implementation outcomes. Unlike complex predictive models, the study emphasizes transparent, easily interpretable AI applications that are already in use in Kazakhstan. The findings confirm that such tools enhance efficiency, mitigate financial risks, and increase resilience in corporate management. This research offers practical recommendations for scaling up AI-based forecasting tools in Kazakhstan’s business ecosystem.