AI-backed agri-fintech is increasingly being used to pilot new rural credit models in Africa, where access to finance remains one of the main obstacles to agricultural transformation.

Artificial intelligence is increasingly used in Africa to diagnose crop diseases, provide early disaster warnings, offer climate-smart advisory services, and power agri-fintech solutions that improve access to credit. According to the discussion paper report How AI can benefit smallholder farmers in Africa, published in September 2025 by the European Centre for Development Policy Management (ECDPM), these technologies are already reshaping parts of the agricultural sector.

The paper says AI tools have already delivered gains in yields, resource-use efficiency, and access to finance. It calls for more ambitious public policies to prevent benefits from being captured solely by large commercial farms.

The ECDPM notes that smallholder farmers “produce more than 70% of the continent’s food and represent the majority of the agricultural workforce.” However, many remain excluded from formal financing due to a lack of conventional collateral, limited banking histories, or distance from bank branches.

Studies show that access to credit can significantly boost the productivity of small farms by enabling input purchases, light mechanisation, and improved market access. A 2020 study by the Research Laboratory in Finance and Development Finance at Benin’s University of Abomey-Calavi estimates that access to credit leads to a productivity gain of around 30.67% for farmers.

How AI feeds new agri-fintech models

The ECDPM report describes artificial intelligence as a “catalyst” transforming how financial institutions assess agricultural risk. AI systems draw on satellite, climate, transactional, and mobile data to better understand farmer profiles. This approach underpins the business model of Apollo Agriculture, an agri-fintech operating in Kenya and Zambia.

The GSMA explains in its GSMA AgriTech AI Blog Series that the company uses an AI-powered credit-scoring engine fed by field data collected via mobile applications, satellite imagery, and, where available, credit bureau data. Apollo Agriculture initially adopted a “lend-to-learn” strategy, deliberately extending loans to a wide range of borrower profiles to build repayment histories, before training its machine-learning models on this data.

Swedfund, the Swedish development finance institution that invested in the company in early 2024, says the platform now enables more than 350,000 smallholder farmers to quickly access inputs and advisory services by combining automated credit scoring with a network of more than 1,000 local distributors.

Other players also leverage data and AI to protect rural livelihoods. Pula, a Kenyan insurtech, positions itself as a “bridge” between insurers and smallholders. The company says it has provided climate insurance to 20.1 million small farmers in 22 countries using high-frequency data, mobile enrolment systems, and AI models that automate parts of the claims process. Pula says its remote sensing and field data tools for pricing and claims management have enabled $133.9 million in payouts to 2.8 million farmers.

In Ghana, Farmerline offers Darli AI, a chatbot available in 27 African languages on WhatsApp, which supports credit monitoring, training, and access to agricultural information.

Impacts on credit access and risk management

The ECDPM report notes that AI-based financial models have already improved access to credit for some groups of smallholder farmers, although benefits remain unevenly distributed and concentrated in specific countries and sectors. A March 2025 article by the Digital Frontiers Institute says that digital services and agri-fintech initiatives have, in some projects, led to yield increases of around 30% to 50% and repayment rates above 85% on digital loans.

These results are encouraging lenders to expand lending to small farmers. AI systems also significantly reduce decision times, allowing farmers to receive near-instant responses on loan eligibility and terms, compared with several days under manual assessment processes.

However, the ECDPM cautions that risk does not disappear. The report highlights concerns around data quality, potential model bias, and the need to maintain human oversight. Apollo Agriculture, for example, relies on a dedicated team to verify data before it is fed into algorithms.

Structural obstacles that still limit AI’s reach

The ECDPM argues that AI could be a powerful catalyst for a more sustainable and equitable agricultural transformation, but only if structural barriers are addressed. The first is infrastructure. In many rural areas, limited connectivity, unreliable electricity supply, and poor network quality continue to constrain the deployment of advanced digital services, a challenge also highlighted by the World Bank.

In a March 2025 blog post, the institution says that “many smallholder farmers lack access to technology and infrastructure. Limited internet connectivity and the high cost of digital infrastructure hamper the development of a tech-enabled agricultural landscape.”

A second barrier is skills and digital literacy. The ECDPM report notes that rural women and young farmers are often the least equipped with digital skills, limiting their ability to use applications even when they are technically available. Data governance and ethical AI frameworks are also emerging as key challenges.

The role of public policies and Africa-Europe partnerships

The ECDPM report calls for “national AI policies centred on agricultural inclusion,” bringing together ministries of agriculture, digital development, and finance around clear objectives to support smallholders. It also highlights several EU-Africa cooperation frameworks, including the Global Gateway programme and Team Europe Initiatives, which could help finance rural digital infrastructure and support the co-creation of AI solutions tailored to local crops, languages, and constraints.

The African Union unveiled its 2026-2035 detailed Plan for agricultural development, in January 2025. The strategy, titled CAADP Strategy and Action Plan (2026-2035), integrates the transformation of African agri-food systems through technological progress, including precision agriculture, digital tools, artificial intelligence, and biotechnology.

In its blog post, the World Bank says that “AI offers transformative potential for agriculture in sub-Saharan Africa. It has the power to enhance efficiency, productivity, and sustainability.” 

“By promoting collaboration, enacting supportive policies, and investing in innovation, the region can leverage AI to achieve food security and foster economic growth,” it adds.

Melchior Koba