The role of information and communication technologies (ICT) in improving microcredit: The case of correspondent banking in Brazil
Bibliographic record
Abstract
Finding ways to efficiently downscale microfinance services is one of the current challenges of Brazilian commercial banks. As commercial banks do not have strong tradition or know-how in this market, the expansion of such operations still depends on the building of specific capabilities and creation of business and technological architectures. This paper discusses how the use of correspondent banking (CB) arrangements can help Brazilian banks to face this challenge and increase their microcredit operations in an efficient way. The particular model of CB adopted in Brazil since 2000 has created an ICT-based business structure for banks downscale financial services out of traditional branches, typically in retail stores such as supermarkets, drugstores, lottery shops, post offices, and so on. The discussion is on how this ICT-based channel can be adapted to scale microcredit delivery. To address the discussion, we focus on one particular case, involving a CB arrangement between Banco do Brasil, one of the most important Brazilian banking institutions, and Banco Palmas, an accredited microfinance institution. This specific case provides an elucidating example of how the Brazilian ICT-based CB model can be used to help scaling up microfinance services, especially microcredit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".