ICT Helping to Scale up Microfinance
Bibliographic record
Abstract
Finding ways to downscale microfinance is one of the current challenges facing commercial banks, especially in developing countries. As banks have a poor knowledge of microfinance, operating in this market will require capacity-building, innovative business models and new technological architectures. This paper discusses how one particular architecture – the Brazilian model of correspondent banking (CB) – is helping banks cope with these challenges. Since the model was created, in 2000, it has allowed banks to downscale financial services outside their traditional branches and establish successful partnerships with local microfinance institutions (MFIs). The authors focus on one particular case involving a partnership between an accredited MFI (Banco Palmas) and two major banks (Banco do Brasil e Caixa Econômica Federal), to make the argument that the Brazilian CB model represents an innovation at the “meso level”, defined by Helms (2006) as the infrastructure comprising a network of service providers necessary to the operation of MFIs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".