Funding Liquidity Risk in Ghanaian Microfinance Institutions (MFI's)
Bibliographic record
Abstract
The paper presents an objective insight into the liquidity challenges faced by MFI’s in Ghana and proposes ways to manage it. It provides an impetus for financial institutions to critically examine their funding performance throughout the year. The survey goes further to reveal that the selected Microfinance Institutions do not conduct periodic evaluation on their financial capabilities to meet the obligations and needs of their customers in terms of readily providing loans and other credit instruments throughout the year. This is because clients make huge withdrawals in the last quarter of the year thus causing a liquidity concentration risk during such periods. Further, the paper suggests that the introduction of viable and innovative products into the market by the various Microfinance institutions coupled with a selective review of prices of products that are doing well in the market is likely to address the funding of the liquidity lapses in Ghanaian Microfinance Institutions. The two processes should however be underpinned by a technological based led monitoring and periodic liquidity trend analysis as it can have an attractive profit augmentation opportunity accessible for the sustainability of MFI’s.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".