By Ayo Susan
First City Monument Bank (FCMB), through its Agritech strategy, plans to deploy artificial intelligence-powered advisory platforms in Hausa, Yoruba and Igbo while supporting AgTech companies developing solutions across Nigeria’s agricultural value chain. Its 2018-2026 Agritech Alumni Impact Report links the strategy to the limited access of African farmers to formal finance, with planned interventions spanning climate-resilient lending, bundled insurance, animal healthcare and expansion of Nigerian agritech startups into Uganda, Ghana, Côte d’Ivoire, Ethiopia and Kenya.
DECISION HIGHLIGHT
FCMB is shifting agritech from a startup-support programme towards a data-enabled financing ecosystem. The strategic value lies in using weather data, soil intelligence, local-language advisory and USSD technology to improve both farmers’ productivity and lenders’ ability to assess agricultural risk.
DECISION MEMO
The central constraint in agricultural finance is not simply the availability of capital. It is the difficulty of evaluating and managing risk across a fragmented smallholder economy. FCMB’s strategy attempts to address that information problem by placing technology between farmers, agricultural conditions and financial services.
The scale of the financing gap explains the rationale. FCMB estimates that agriculture contributes about 24 percent of Nigeria’s gross domestic product (GDP), while only six percent of African farmers have access to formal credit. The disconnect suggests that conventional lending models are poorly matched to agricultural realities.
AI-powered advisory in Hausa, Yoruba and Igbo could lower an important access barrier by delivering farm information in languages familiar to users. Weather intelligence and soil data can further improve production decisions while potentially giving financial institutions better information for credit assessment.
That creates a potential feedback loop: better data can improve farm productivity; improved production records can strengthen credit assessment; and better risk information can make agricultural lending more commercially viable.
The bank’s programme has consequently evolved beyond a startup competition into an ecosystem involving innovators, investors and development partners. Its proposed climate-resilient financing, insurance and animal healthcare solutions extend the model from credit provision towards broader agricultural risk management.
The planned expansion of Nigerian AgTech businesses into other African markets also gives the strategy an export dimension. If domestic technology can be adapted across similar agricultural systems, Nigerian firms could develop scalable products while FCMB gains exposure to a wider agricultural-finance market.
The strategy is therefore less about replacing conventional banking with AI than about improving the information infrastructure on which agricultural finance depends. FCMB describes the objective as building “a de-risked, data-driven, and sovereign future for food and agriculture in Africa.”
Its partnerships with the Netherlands-based development finance institution FMO, the United Nations Development Programme and Mastercard Foundation provide an additional institutional channel for scaling the model.
DATA BOX
- Agriculture’s reported GDP contribution: About 24%
- African farmers with formal credit access: 6%
- AI advisory languages: Hausa, Yoruba, Igbo
- Programme period: 2018-2026
- Technology channels: AI, weather data, soil intelligence, USSD
- Planned markets: Uganda, Ghana, Côte d’Ivoire, Ethiopia, Kenya
- Planned solutions: Climate-resilient finance, insurance, animal healthcare
- Strategic model: AgTech, finance, investors and development partners
WHO WINS / WHO LOSES
Who wins: Smallholder farmers, AgTech companies, agricultural lenders and insurers if better data reduces financing and production risks.
Who loses: Farmers and businesses excluded by conventional credit assessment remain disadvantaged if technology adoption, digital access or data quality remain weak.
POLICY SIGNALS
Agricultural finance policy increasingly needs to incorporate digital identity, alternative data, climate information and technology-enabled risk assessment. Infrastructure that makes agricultural data usable and shareable could improve credit allocation.
INVESTOR SIGNAL
The opportunity extends beyond farm lending into AgTech platforms, agricultural data, embedded finance, insurance and cross-border technology exports. Scalable businesses that convert agricultural data into commercially useful risk intelligence could become increasingly investable.
RISK RADAR
AI cannot eliminate agricultural risk. Poor connectivity, low digital literacy, unreliable data, climate shocks, repayment failures and weak adoption could limit the model. The critical measure will be whether technology produces measurable increases in credit access, farm productivity and repayment performance.