Lateephah/Loan-Default-Risk-Predictor-Micro-Lenders-POS-Agents-Nigeria-
Domain:
socioeconomicRecord type:
model
Creator:
Lat
Host:
A tabular machine learning model that predicts the likelihood of loan default, built for Nigerian micro-lenders and POS (Point-of-Sale) agents who need a fast, explainable repayment-risk check before disbursing small, short-tenor loans.
# Loan Default Risk Predictor for Micro-Lenders & POS Agents (Nigeria)
A tabular machine learning model that predicts the likelihood of loan default, built for
Nigerian micro-lenders and POS (Point-of-Sale) agents who need a fast, explainable
repayment-risk check before disbursing small, short-tenor loans.
**▶ Watch the 3-minute demo video**
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## Table of Contents
- Problem Statement
- Solution Overview
- Data
- Methodology
- Results
- How to Run
- Limitations & Future Work
- Acknowledgments
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## Problem Statement
Across Nigeria, POS agents and micro-lenders extend small, short-term loans to informal
traders, artisans, and small business owners often with little to no formal credit history
to underwrite against. A wrong call is costly in either direction: lending to a high-risk
borrower risks non-repayment, while rejecting a genuinely low-risk borrower loses income and
excludes someone who may be perfectly creditworthy.
This project builds a lightweight, explainable risk-scoring model that a micro-lender or POS
agent could realistically use at the point of a lending decision, fed either a single
applicant's details (manual input) or a batch of applicants (CSV upload).
## Solution Overview
- **Input:** borrower demographic, business, POS-transaction, and loan-request data via
manual entry or CSV upload
- **Output:** a default-risk probability (0–100%), a risk band (Low / Medium / High), and
the key factors behind the score
- **Models compared:** Logistic Regression, Random Forest, and XGBoost, each evaluated under
three class-imbalance strategies (baseline, class weighting, SMOTE), nine variants total
- **Deployed model:** XGBoost with class weighting (see Results for why)
## Data
**No public, row-level loan-default dataset exists for the Nigerian micro-lending / POS-agent
market** that kind of individual borrower data is proprietary to lenders and protected under
banking privacy regulation. This was confirmed by checking the Central Bank of Nigeria …