Research
Credit scoring without credit histories
AfriScore is a research-driven credit intelligence engine built to answer one question: how do you assess creditworthiness in markets where most adults have never interacted with a formal credit bureau?
The Problem
In East, South, and Central Africa, a large majority of adults have no formal credit file, yet generate rich digital footprints through mobile money, airtime, and device usage. Traditional scoring models ignore this data because it doesn’t fit the "credit history" mould — leading lenders to either reject invisible applicants or rely on crude heuristics. AfriScore treats everyday digital behavior as a legitimate signal of creditworthiness.
The coverage gap
Credit bureau coverage across much of the region remains low, particularly for informal-economy participants — even in markets with the most developed bureau infrastructure, a significant share of the adult population remains unscored.
Mobile money ubiquity
Mobile money adoption across Sub-Saharan Africa is extensive, with hundreds of millions of registered accounts. That transaction data is a proxy for income stability, savings behavior, and financial discipline — the raw material AfriScore is built to use.
A Two-Stage Approach
Because there is no large, labeled dataset of "good" and "bad" borrowers with mobile money footprints readily available, AfriScore uses a two-stage pipeline that separates pattern learning from probability estimation.
Stage 1
Synthetic data generation
A synthetic population is constructed using empirical distributions derived from anonymized mobile money data patterns across East, South, and Central Africa. Key behavioral variables — inflow, outflow velocity, savings rate, airtime regularity, device type — are modeled to preserve realistic correlations, with a probabilistic default model assigning plausible repayment outcomes for training.
Stage 2
Ensemble model & calibration
A Random Forest ensemble (200 trees, max depth 8, min samples split 50) captures non-linear interactions in the synthetic data. Once real repayment outcomes are available from a pilot deployment, isotonic regression (Pool Adjacent Violators algorithm) will calibrate raw scores into well-calibrated probabilities of default.
Target Performance Metrics
These are engineering targets for the calibration phase, not results from live deployment — AfriScore has not yet processed real repayment data. They will be replaced with measured results once pilot calibration begins.
Stress Testing Plan
The model is designed to be evaluated against adversarial scenarios before any live lending use — missing data, noisy inputs, and distribution shift are all expected conditions in real deployment, not edge cases.
Feature ablation
Measuring how much accuracy is lost when the most important individual features are removed.
Data sparsity
Testing performance when a large share of input fields are missing or unavailable.
Distribution shift
Testing whether the model generalizes across different regional behavioral distributions.
SHAP stability
Confirming that explanation rankings remain consistent across resampled populations.
Fairness & Explainability
Every AfriScore decision is designed to ship with SHAP-based feature contributions and counterfactual explanations. Fairness monitoring across region, device type, and gender (where data permits) is planned as part of the pilot calibration process, with results to be published openly.
Publications
The Architecture of African Financial Infrastructure
A book examining the structural foundations of financial systems across Africa — the broader thesis underlying AfriScore’s approach.
View on Amazon →Alternative Data and Credit Risk Assessment in Emerging African Economies: A Quantitative and Regulatory Framework
The academic foundation for AfriScore’s scoring methodology and regulatory positioning.
Read on SSRN →The 2026 State of Alternative Credit Scoring in Sub-Saharan Africa
Mapping the data, the market, and the policy frontier for alternative credit scoring across the region.
Read the policy paper →