The emerging role of AI tools in smallholder finance

By Tamsin Zandstra, Berber Kramer, and Kate AmblerJune 24, 2026

Key takeaways

• AI can expand access to finance for smallholders, a key element of food system transformation. AI tools can improve risk assessment, lower costs, and help lenders reach farmers without traditional credit histories.
• Real-world applications show strong potential. Tools using satellite data, machine learning, and digital images are increasing credit uptake, insurance adoption, and investment decisions.
• Risks and gaps must be addressed. Limited data, unequal access—especially for women—and weak governance can deepen exclusion without responsible design and oversight.

Improving smallholders’ access to finance is a crucial element of food system transformation. Farmers in many low- and middle-income countries (LMICs) lack consistent access to loans, insurance, and other important financial services, leaving them more vulnerable to shocks and unable to build resilience or invest in more innovative, productive, and sustainable practices.

Among the obstacles they face are high transaction and monitoring costs (because borrowers are geographically dispersed), small loans, and lenders who often lack the tools to assess risk effectively. Insurance products are often poorly designed for small farmers’ needs. Meanwhile, for financial institutions, agriculture presents serious lending risks, including weather shocks, pests, and disease outbreaks. Other problems include policy instability, weak infrastructure, and macroeconomic volatility in LMICs.

Artificial intelligence (AI) applications show promise in addressing these constraints by improving how financial institutions assess, price, and manage agricultural risk. The experience of IFPRI and partners in AI projects targeting smallholders in several countries shows that AI applications can reduce financial uncertainty and make returns more predictable for banks, insurers, investors, and other value chain actors, ultimately helping smallholders gain access to more reliable, consistent financial support.

AI tools are increasingly employed across food systems and in various dimensions of food system transformation, from precision farming to automating and expanding extension advisory services. However, they face a number of obstacles, particularly in LMICs, including limited access to technology, incomplete national and local data essential for model training, and issues of reliability and trust. These challenges must be weighed and addressed in any AI project targeting smallholders.

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