A farmer can own productive land, cultivate crops, supply a processor, and earn income every season, yet still be ineligible for a bank loan. An agricultural trader can have confirmed orders from buyers but lack the working capital to purchase produce. A smallholder can know exactly which seeds and fertilisers are needed to improve yields but be unable to afford them at planting time.
Across Kenya, a growing range of agricultural technology and financial technology companies is attempting to address these problems by changing how agricultural businesses access money, inputs, equipment and markets.
Their approaches vary. Some use digital data and machine learning to assess borrowers who lack conventional credit histories. Others finance agricultural inputs directly, spread the cost of irrigation equipment over time, accelerate payments for delivered produce, or help farmers establish transaction records that financial institutions can use when evaluating loan applications.
The opportunity is significant. The International Finance Corporation’s 2024 report, Scaling Up Farmer Financing through Agtechs in Sub-Saharan Africa, estimated the financing gap for smallholder farmers and agricultural small and medium-sized enterprises across the region at approximately US$117 billion. The study examined digitally enabled agricultural businesses across seven African markets, including Kenya and Nigeria, highlighting their potential to connect underserved agricultural enterprises with financial services.
Kenya provides an important case study because its agricultural technology ecosystem demonstrates how finance can be integrated with the practical business of farming. The emerging model is not simply about putting loan applications on mobile phones. It is about using technology to make agricultural activity more visible, measurable, and potentially less risky to finance.
From Land Titles to Digital Creditworthiness
One of the most persistent barriers to agricultural finance is the way lenders assess risk. Traditional lending often depends on collateral, formal income statements, credit histories, and other documentation that smallholder farmers and informal agricultural businesses may not possess. Farming also involves seasonal cash flows, uncertain yields, fluctuating commodity prices, and exposure to extreme weather. For lenders, verifying a small farmer’s capacity to repay can be expensive relative to the size of the loan.
Technology offers an alternative approach: assess the business activity behind the borrower rather than relying exclusively on conventional financial records or physical assets.
Apollo Agriculture illustrates this model. The Kenyan technology company uses machine-learning credit models to assess farmers seeking financing for agricultural inputs. Approved customers can obtain seeds, fertiliser and other products through a digital ordering system and collect them from participating agro-dealers. The company also combines input financing with agricultural training and insurance for farmers purchasing on credit.
Apollo says it works with more than 1,000 agro-dealers, giving its customers access to products through local distribution networks. Its model addresses two problems simultaneously: the inability to afford quality inputs upfront and the difficulty of obtaining conventional agricultural credit.
The significance is that the financing is connected directly to productive investment. Rather than disbursing cash, the company helps farmers acquire resources intended to improve their production.
However, this approach does not eliminate lending risk. Credit models still need reliable information, appropriate loan limits, and repayment arrangements that reflect local growing conditions. The value of the technology ultimately depends on whether financed inputs improve farm performance sufficiently to justify the cost of borrowing.
A related development is emerging in financing for agricultural businesses beyond the farm gate. In July 2026, Kenya News Agency reported on Avenews, a fintech company using artificial intelligence and machine learning to assess the creditworthiness of small and medium-sized agribusinesses through transaction data. The model is intended to help businesses that struggle to obtain conventional bank loans because they lack sufficient collateral.
This distinction matters. Agricultural traders, aggregators, processors and input distributors need working capital to purchase produce, pay suppliers and fulfil contracts. Their financing needs can be substantial even when they have viable operations and identifiable customers.
If lenders can assess verified transactions, sales patterns and business performance more efficiently, they may be able to finance enterprises that traditional credit assessments overlook. For agricultural supply chains, improved access to working capital can also help farmers by enabling buyers and aggregators to purchase produce more consistently.
Financing Inputs Before the Harvest
For many smallholders, the most urgent financial problem is not necessarily access to unrestricted cash. It is the inability to pay for inputs when they are needed.
Seeds and fertiliser must often be purchased before planting, while revenue may not arrive until several months later. A farmer who misses the planting window because of inadequate funds may lose an entire production opportunity.
Safaricom’s DigiFarm platform demonstrates how digital technology can bring agricultural finance closer to the production cycle. Its services include access to input loans, cash advances, agricultural information, insurance and market connections, alongside services such as soil testing and mechanisation.
A particularly useful example is the DigiFarm input-financing product offered with Sidian Bank. According to Safaricom’s published product information, eligible farmers can access credit for inputs and selected agricultural services, with repayment periods tailored to different value chains.
The published terms illustrate both the opportunities and the limitations of the model. For the specified product, farmers are required to make a 17.5 per cent down payment, while platform fees range from 1 per cent to 2.5 per cent depending on the value chain. Interest is charged monthly, with the applicable rate communicated by the bank. The product also incorporates insurance intended to cover specified agricultural risks.
These conditions are important because digital access should not be confused with affordable access. A farmer may qualify for a loan but still be unable to meet the initial contribution. Fees and interest can also reduce the profitability of the investment, particularly when yields or market prices disappoint.
Nevertheless, linking financing to approved agro-dealers, specified inputs and repayment schedules aligned with crop cycles can make agricultural credit more practical. It creates a clearer relationship between the loan, the intended productive activity and the expected source of repayment.
The model also shows why partnerships matter. Technology platforms can identify and organise farmers, but banks and other regulated financial institutions may still provide the actual credit. Insurance providers, input suppliers and produce buyers can each address different parts of the risk.
When a Future Harvest Becomes Financial Security
Perhaps the most important innovation is the attempt to make a farmer’s future income more useful to lenders. Collateral does not always have to mean land or a building. Depending on the legal arrangements and the lender’s requirements, documented receivables, warehouse receipts, confirmed purchase contracts and other verifiable commercial assets may support financing.
Ketha Africa is developing a model around the relationships between smallholder farmers, aggregators, processors and buyers. Its platform digitises agricultural transactions, tracks produce deliveries and facilitates payments across the value chain.
According to the company, accepted deliveries to processors can create contractual value that farmers and other participants may use to support access to finance. Ketha also says its payment solution can reduce waiting periods by as much as 45 days in some value chains.
The underlying mechanism is straightforward. A farmer who delivers produce to a recognised buyer may have a more verifiable commercial claim than one who sells informally without documented transactions. A processor with confirmed demand from downstream buyers may also be better positioned to demonstrate future revenue.
Digital systems can record these relationships, verify deliveries and help financiers understand when money is expected to move through the supply chain. This creates the possibility of financing against future cash flows rather than depending entirely on a borrower’s ownership of physical assets.
However, the distinction between evidence of a future payment and legally enforceable collateral is critical. An accepted delivery or purchase contract does not automatically guarantee a loan. Financiers must still evaluate the buyer’s creditworthiness, contract terms, quality requirements, payment obligations, and the possibility of disputes or non-payment.
Technology can improve visibility and documentation, but legal enforceability and commercial reliability remain essential.
Turning Agricultural Transactions into a Financial Identity
A different but complementary approach is to build a digital financial history for farmers whose businesses have operated largely outside formal financial systems.
In July 2026, the Kenya National Farmers’ Federation reported a farmer-digitisation initiative involving Mastercard, KCB Bank Eldoret and CropSoko under the Agri-SME Catalytic Financing Mechanism.
The initiative, implemented through the wider Mobilizing Access to the Digital Economy Alliance, is helping cooperative members record purchases, sales, production activity and other transactions electronically through KENAFF’s Sauti ya Mkulima platform.
The objective is to build a verifiable record of agricultural activity that financial institutions can use when assessing farmers for financial services. This addresses a problem that is often overlooked in discussions about agricultural lending: many farmers are not necessarily without economic activity. They are without sufficient documented evidence of that activity.
A farmer may sell milk every morning, buy fertiliser several times a year and consistently deliver maize to a cooperative. If these transactions are recorded reliably, they can provide evidence of business continuity, purchasing behaviour, sales volumes and potential repayment capacity.
The initiative is working towards improving the digital readiness and bankability of 250,000 smallholder farmers, according to KENAFF. The long-term opportunity extends beyond credit. Better records can support insurance underwriting, buyer verification, supply-chain traceability and more informed business decisions.
But the quality of the data matters. Records must be accurate, sufficiently comprehensive, and accessible with appropriate consent. Farmers who conduct transactions outside participating platforms may otherwise remain underrepresented, while poorly designed scoring systems could reproduce existing inequalities.
Financing the Equipment That Makes Farming More Productive
Access to working capital is only one part of agricultural finance. Farmers also need equipment that can increase productivity and reduce their exposure to weather conditions.
SunCulture, a Kenyan solar-irrigation company, addresses this problem by combining productive equipment with financing. Its Pay-As-You-Grow model spreads the cost of solar-powered irrigation systems over time, reducing the need for farmers to pay the entire purchase price upfront.
The financial innovation is especially relevant where irrigation can support dry-season production, crop diversification or more reliable water access. A productive asset may generate value across several growing seasons, making staged payments more appropriate than requiring the farmer to finance the entire investment immediately.
In December 2025, SunCulture announced SunCulture Protect, a climate-insurance product developed with Humanity Insured, APA Insurance and IBISA. The product is integrated into its solar-irrigation financing arrangements and is designed to respond to specified climate triggers, including drought and excessive rainfall.
Under the published arrangement, qualifying payouts for customers financing equipment can be applied to outstanding loan balances, while eligible cash and post-loan customers can receive payouts through mobile wallets.
This combination of equipment financing and climate protection illustrates a broader principle: agricultural finance works better when it recognises the risks that affect a farmer’s ability to repay.
A loan for irrigation equipment may improve production, but drought, flooding, crop disease or falling prices can still undermine the investment. Financing that incorporates appropriate insurance can help reduce some of these risks, although the effectiveness of protection depends on policy coverage, trigger conditions, exclusions and payout adequacy.
The Business Model Behind the Technology
The companies operating in this space are not all solving the same problem, nor do they necessarily earn revenue in the same way.
Input-financing businesses can generate income through financing arrangements, product distribution or related services. Equipment providers can earn from hardware sales and financing. Platforms that connect farmers to processors may charge for services, transactions or value-chain management. Fintech companies can earn revenue from financing arrangements with agricultural businesses.
These models depend on different commercial relationships, but they share a common objective: making agricultural activity easier to measure, finance and transact.
Their success also depends on partnerships. A technology company may be good at collecting data but lack the balance sheet to lend at scale. A bank may have capital but lack the infrastructure to assess thousands of dispersed farmers economically. An insurer may be able to cover specific risks but need better information about farms and production conditions.
When these capabilities are combined, technology can reduce some of the costs that make small agricultural loans unattractive to conventional lenders.
Yet there is a significant commercial challenge. Many agricultural technology companies themselves need substantial working capital to finance customers, purchase inputs, distribute equipment, or advance payments before buyers settle their invoices. The IFC has identified limited access to working capital as a constraint on the growth of agricultural technology businesses.
This means that closing the financing gap requires more than convincing banks to lend to farmers. It also requires financing the intermediaries that connect farmers to markets, inputs, and financial services.
The Risks: Digital Lending Is Not Automatically Inclusive
The promise of agricultural fintech or agritech as it is labelled must be evaluated against the realities farmers face.
First, technology cannot eliminate agricultural risk. A sophisticated credit model cannot prevent a failed rainy season, reverse a pest outbreak or guarantee a profitable market price. If loans are structured without adequate consideration of these risks, digital lending can make debt easier to obtain without making it safer to repay.
Second, the cost of credit matters as much as its availability. Down payments, interest, platform fees, insurance premiums and penalties can affect the final cost of financing. Farmers need clear information about the total amount repayable and the consequences of default.
Third, digital exclusion remains possible. Farmers may lack smartphones, reliable connectivity, digital literacy or sufficient transaction histories. Systems that rely too heavily on digital records could exclude those who need financing most.
Fourth, data governance is essential. Farmers should understand what information is collected, how credit decisions are made, which institutions receive their records, and how they can challenge inaccurate information.
Finally, access to credit must translate into measurable economic improvement. The most important indicators are not the number of applications processed or accounts registered. They are whether farmers receive financing when they need it, obtain inputs at competitive prices, improve yields or income, withstand adverse seasons, and repay loans without falling into unsustainable debt.
What Kenya’s Experience Means for Africa
Kenya’s experience offers lessons for agricultural markets across the continent.
The first is that agricultural finance should be built around the economics of specific value chains. Dairy farmers may receive income regularly, while maize farmers may wait several months for harvest revenue. Input suppliers, aggregators and processors also have different working-capital requirements. Financial products should reflect those differences.
The second is that digital records can be valuable economic infrastructure. A farmer’s sales history, cooperative transactions and verified deliveries can help establish credibility with lenders and buyers. Building that infrastructure requires trusted institutions, reliable data and incentives for farmers to participate.
The third is that financing should be integrated with the wider agricultural value chain. Loans are more useful when farmers can obtain quality inputs, receive sound agronomic advice, manage production risks and access reliable buyers.
For other countries, the opportunity would be to connect existing mobile-money and banking infrastructure, farmer cooperatives, agro-dealers, commodity aggregators, processors and agricultural insurers into systems that make farmers easier to serve commercially. The specific models would need to reflect specific regulatory environments, value chains, payment infrastructure and patterns of land tenure.
Ultimately, the question is not whether Kenya’s models can be copied directly. It is which mechanisms can be adapted to different African markets and supported by sustainable financing, credible data, and strong farmer protections.
Learning Points
Kenya’s emerging agricultural fintech ecosystem points towards a potentially important shift in how farming is financed. Instead of treating land ownership and conventional banking history as the primary evidence of creditworthiness, technology is creating opportunities to evaluate farmers and agribusinesses through transactions, production activity, verified sales, and expected cash flows.
Apollo Agriculture demonstrates how input financing can connect credit to productive investment. DigiFarm shows how digital platforms can combine financing with agricultural services. Avenews is applying data-driven assessment to agribusiness working capital, while Ketha Africa is using documented produce deliveries and buyer relationships to help make future cash flows more visible. SunCulture illustrates how equipment financing can be combined with climate protection, and the KENAFF-led initiative shows how digital records can help build farmers’ financial identities.
The larger lesson is that the future of agricultural finance may depend as much on making farmers financially visible as on making money available to them.
But visibility alone is not enough. Data must lead to suitable financial products, affordable borrowing costs, reliable markets and better agricultural outcomes. Without those conditions, technology risks digitising the same barriers that have historically excluded smallholders.
The real measure of success will be whether farmers can use these systems to invest productively, withstand shocks and build viable businesses, not simply whether they can obtain a loan.
For Africa, the opportunity is to move from financing agriculture as an exceptional lending risk towards financing it as a measurable, commercially connected economic sector. Kenya’s experiments offer a compelling starting point, but their lasting significance will depend on evidence of improved farmer incomes, sustainable repayment and broader access to finance.
