An Assessment of Credit Risk Management Practices of Adansi Rural Bank Limited
Bibliographic record
Abstract
Rural banks in Ghana are not exempted from the risk exposures associated with managing credit. Given their importance to the economy, appropriate measures should be taken to mitigate credit risk exposures of rural banks in the country. The study critically examines the credit risk management practices of rural banks in Ghana making reference to Adansi Rural Bank Limited. The study was carried out to examine the credit management practices, credit policies and strategies for managing credit as well as challenges faced in this practice and to recommend solutions that will mitigate the credit risk exposures of Adansi Rural Bank Limited. The researcher used a purposive sampling technique to select a sample size of forty respondents which comprised of branch managers and credit officers from four different branches of the rural bank. The researcher used a well structured questionnaire and a face to face interview to collect primary data for this study. The researcher employed both primary and secondary data in the study. Descriptive statistical tools were used in analysing the data collected. The researcher discovered that Adansi Rural Bank Limited had implemented a rigorous credit risk management policy. This included; loan appraisal, use of collateral and checking the credit history of borrowers. The results of the study revealed that, rural banks that have implemented rigorous credit risk management policies were exposed to few challenges in managing credit risk as compared to rural banks with poorly implemented credit risk management policies. This affirms the point that a comprehensive credit risk management system should be adopted and implemented well by rural banks in Ghana.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".