Micro-Lenders Sub-Prime Lending in South Africa Implications for Credit Bureaux and Credit Scoring By Raymond Anderson Senior Manager – Credit Scoring
Bibliographic record
Abstract
The purpose of this report is to provide an overview of the sub-prime market in South Africa, which within the country is called “microlending”. It covers the history, current situation, and some of the challenges posed to the various market players, including micro-lenders, credit bureaux, banks, retailers, and others. This would be of particular interest to people involved in sub-prime lending in first world countries, and those from developing economies. The South African economy ranks 30 th in the world by size, and is the largest in Africa. It may be relatively small by first-world standards, yet it has a sophisticated banking system that is similar to those of the UK, Australia, and Canada. Indeed, many of the electronic banking services are superior due to a prolonged focus on automation to reduce costs. Four major banks dominate the mainstream lending market, while retailers of various products lend to facilitate sales. For many years retailers co-operated through a Consumer Credit Association to share data, while most banks relied solely on own and negative bureaux data. The two major credit bureaux in the country are TransUnionITC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".