Empowering Africa’s Disfranchised SMEs: Machine Learning-Based Credit Scoring for Informal African Merchants
Bibliographic record
Abstract
Abstract Access to working capital is critically limited in the informal retail sector due to the absence of appropriate financial products. Improved credit scoring can thus facilitate increased access to working capital, supporting the growth and sustainability of small businesses in the informal sector. This study addresses this challenge by developing tailored credit scoring models for informal merchants using supervised learning techniques. Leveraging data from a financial technology company in Lesotho (South Africa), we applied logistic regression and support vector machines to predict the likelihood of loan defaults among merchants. Our methodology involved the evaluation of six logistic regression models and twelve support vector machine models, assessing their effectiveness in default prediction. The results provide a robust tool for more accurate assessment of creditworthiness, reducing the risk of lending to potential defaulters. The study underscores the potential of supervised learning methods to create impactful financial solutions and suggests a pathway towards narrowing the financial inclusion gap in the informal African economy. This approach not only aids in risk reduction for lenders but also empowers a critical segment of the economy by enabling better financial support and growth opportunities for informal sector merchants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".