Internal Financial Risk Management In Microfinance Companies: A Case Study Of Akuapem Rural Bank, Ghana
Bibliographic record
Abstract
The operations of Microfinance Institutions (MFIs) in Ghana have recently come under serious public scrutiny. This position was fairly caused by Bank of Ghana’s (BOG’s) announcement regarding 70 microfinance companies whose provisional licenses were revoked BOG (2016). This led to the closure of DKM Diamond Microfinance and some other microfinance companies in the country. This worsening circumstance surrounding the microfinance industry calls for the need to provide practical knowledge on the use of financial analysis tools to manage internal financial risks of the microfinance industry. Data from Akuapem Rural Bank (AKRB) financial statements for the period of 2008 to 2015 (refer to appendix) was analysed using regression analysis, descriptive statistics, trend analysis and ratios. It was observed that the profitability of AKRB is greatly influenced by credit risks, bank size, interest income growth and debt-ratio. The study also revealed that AKRB had comprehensive and adequate risk management structures in place in managing its credit and other operational risks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".