Farm reality
Land, production and the capacity to operate need verification on the ground.
Farmer credit scoring,
built around real performance.
Our goal is an AI-based credit assessment model grounded in verified farm activity, financial records and repayment outcomes.
Early stage · Evidence foundation first · Model development ahead
FYIELDEVIDENCE FRAMEWORK
FARM PROFILE
Evidence for
farmer credit.
- 01Identity & assets
- 02Production
- 03Markets & cash flow
- 04Financing readiness
EVIDENCE → MODEL → CREDIT ASSESSMENTRead the overview
01 / THE EVIDENCE GAP
An evidence gap,
before a financing decision.
Land, operating capability and sales do not always appear together in a conventional financial record.
Business records
Sales, costs and supporting documents can be fragmented across different sources.
Financing decisions
Institutions need a consistent picture of the business, its risks and the gaps that remain.
FARMERBusiness activity
FYIELDVerified evidence
INSTITUTIONOwn assessment
Consent and source records accompany the profile. The financial institution retains the lending decision.
02 / THE END GOAL
The end product:
farmer credit intelligence.
Our goal is a farmer credit score banks can use in their own assessments, informed by verified performance and outcomes beyond conventional credit histories.
Farm verification is the starting point. The long-term product is a tested, repeatable assessment of farmer credit risk, built from evidence about how a farm operates and performs.
PRODUCT VISIONAI-BASED CREDIT ASSESSMENT
- 01
Build the evidence
Connect verified farm records, financing outcomes and relevant external data with permission.
- 02
Develop the model
Train and test models that turn those records into an assessment of farmer creditworthiness.
- 03
Validate its usefulness
Evaluate reliability, fairness and practical value with institutions before operational use.
The scoring model is planned. FYIELD does not currently operate a validated credit-scoring model. Financial institutions retain lending decisions.
03 / THE OPERATING LIFECYCLE
The farm financing lifecycle.
Each production cycle can build the evidence needed for future model development, from verified records to observed outcomes.
- 01
Screen
Understand the farmer, the business and the financing need.
Business context - 02
Collect
Gather consent, identity, production and financial records.
Source documents - 03
Verify
Check documents and observations from the field.
Findings & open gaps - 04
Prepare
Build a case file with findings, risks and unresolved questions.
Institution-ready case - 05
Connect
Support the institution’s assessment and financing process.
Institution’s decision - 06
Monitor
Record performance and permissioned repayment outcomes.
Cycle observations
The onboarding and verification process is documented. A complete institutional pilot is the next milestone for financing and outcome collection.
04 / THE EVIDENCE
A record with a source.
Each profile is designed to connect farm information with supporting records and a verification status.
| Evidence area | What the profile records |
|---|---|
| 01 Identity and assets | Business identity, land, location and infrastructure |
| 02 Production | Crops, capacity, historical yields and operating costs |
| 03 Markets and cash flow | Sales evidence, buyer relationships and financial records |
| 04 Financing readiness | Use of funds, repayment assumptions, risks and unresolved gaps |
Source recordsVerification statusUnresolved gaps
Verified observations and open questions remain distinguishable.
Our data approach05 / THE DATA FOUNDATION
Connect farm performance
with financial outcomes.
The planned model would combine farm activity, financing and repayment outcomes with relevant external data to build a fuller picture of farmer creditworthiness.
VERIFIED FARM ACTIVITY
Operating performance
Production, sales, costs and capacity
FINANCIAL & EXTERNAL RECORDS
Financial & contextual data
Partner financial history and relevant external sources
OBSERVED FINANCING OUTCOMES
Repayment behaviour
Permissioned repayment and default outcomes across cycles
WITH CONSENT AND TRACEABILITY
Connected records over time
The evidence base for developing and testing the model
AI model development → Institutional validation
Planned architecture. Partner access and integration remain to be agreed. External sources will be assessed for relevance, quality and permitted use; model development requires sufficiently representative outcome data.
06 / WHERE WE ARE
Building toward a first
institutional pilot.
CURRENT STAGE / EARLY
A registered Nigerian company with a defined verification process and an institutional pilot to build.
- Established
- Nigerian incorporation and structured onboarding materials.
- In progress
- Farm case preparation and conversations with potential partners.
- Next milestone
- A complete monitored pilot with an institution and usable outcome data.
THE FIRST INSTITUTIONAL PILOT
One complete cycle to learn from.
- 01
Agree the case
Select an eligible farm and define the institution’s information requirements.
- 02
Deliver the evidence
Complete verification, document risks and track the decision.
- 03
Observe the cycle
Monitor farm performance and receive permissioned outcome data.
- 04
Review the results
Assess data quality, operating cost and usefulness to the institution.
07 / THE COMMERCIAL APPROACH
Initial facilitation.
Future credit intelligence.
INITIAL FACILITATION PROPOSAL5%
Proposed facilitation fee
Subject to agreement
The initial proposal links fees to successful financing facilitation.
The fee base, payer, timing and responsibilities will be agreed with each partner.
FUTURE PRODUCT DIRECTION
Credit assessment tools · Monitoring · Data services
Subject to development and validation. Future pricing and delivery remain to be defined.08 / THE TECHNOLOGY ROADMAP
From verified records
to a tested credit model.
The AI-based credit assessment model must earn its reliability through suitable data, observed outcomes and institutional validation.
- 01
Reliable records
Standardise collection, verification and traceability.
- 02
Observed outcomes
Build histories of operations, financing, repayments and defaults.
- 03
Develop & test AI
Train and test models using farm histories, financial outcomes and relevant external data.
- 04
Institutional validation
Evaluate reliability, fairness and usefulness before operational use.
Planned roadmap. Better predictive performance is an objective to test. FYIELD does not currently issue official credit scores or make lending decisions.
09 / THE PARTNERSHIP OPPORTUNITY
Help build a new approach
to farmer credit.
Pilot execution
Work with an institution to assess farm cases and complete monitored financing cycles.
Permissioned outcome data
Connect financial and repayment records with verified farm activity.
Model research & validation
Support data collection, model development and testing with institutions.