A faith-based microfinance NGO considered ML to predict loan defaults. They rejected at Receive when they found CGAP guidelines already addressed this.
Adopted CGAP's published methodology instead of building a proprietary model.
Loan officers in Nairobi and Kampala are exhausted. They're managing 300+ borrowers each. Default rates are rising — not because borrowers are dishonest, but because drought and inflation are crushing them. Leadership is anxious about institutional solvency. Borrowers feel like numbers in a spreadsheet, not neighbors.
Christ would see that the borrowers are not the risk — the system that demands repayment during a drought is the risk. He'd see loan officers who used to know every borrower by name now reduced to data-entry clerks. He'd see leadership treating a moral problem as a technical one.
What you've named is not a credit-scoring problem. It's a discipleship problem dressed up as a risk problem. The institution wants protection; the borrowers need presence. Before you ask whether AI can predict default, ask whether your officers still have time to visit a borrower's farm. The most accurate predictor of repayment is usually the relationship — and that's the thing automation will quietly erode.
An ML model trained on five years of loan history to predict repayment likelihood, scoring every new loan application for our loan officers in Kenya, Tanzania, and Uganda.
Loan officers like Grace in Kisumu are managing 320 borrowers and can't tell which ones are at risk until they default. Leadership wants a screening signal so officers can intervene earlier. Real users: 47 loan officers across three countries; ~14,000 active borrowers, mostly smallholder farmers and market vendors.
Yes — partly. Smaller caseloads would solve more than a model would. The real bottleneck is that we cut field officer headcount in 2023 to reduce overhead. We're trying to use AI to recover what we gave away in staffing.
Stated problem: 'we can't predict defaults.' Real problem: 'we've eroded the relational infrastructure that made our model work, and now we're rationalizing the erosion with technology.'
Borrowers flagged as 'high-risk' by an opaque model would be denied loans without explanation. In peer-group lending, one denial cascades — the whole group's standing weakens. We'd be encoding a drought year into a permanent credit memory.
Yes. Rehiring two field officers per region. Restoring monthly borrower group visits. Adjusting repayment schedules seasonally instead of monthly. We haven't tried any of these because they cost more than the model would.
This team did the work most teams skip — they let Receive end the conversation. CGAP's framework is freely available, peer-reviewed, and field-tested. Building their own version would have been ego-driven, not service-driven. The discipline here is naming that 'we could build this' is not the same as 'we should build this' — especially when faithful work already exists in the public commons.
Start from this example as a draft. Every field will be pre-filled — edit freely. Your own context will surface as you go.
These examples are illustrative. Real discernments will be more complex, more painful, and more specific to your context. The library helps you see the shape of the work.
Examples paraphrased from common patterns observed in faith-based development AI projects. Not based on any single real organization.