The Effectiveness of Credit Risk Mitigation Strategies Adopted by Ghanaian Commercial Banks in Agricultural Finance
Bibliographic record
Abstract
Lending to the agricultural sector by commercial banks in Ghana is characterized by high credit risk. Empirical evidence suggests that commercial banks in Ghana have credit risk management (CRM) challenges. This study explores the credit risk mitigation strategies adopted by commercial banks to minimize credit risk in agricultural finance in Ghana. The study adopted a mixed-method approach using a survey questionnaire and interview instruments. The findings indicate that some of the strategies used by commercial banks to mitigate credit risk in agricultural finance do not meet commercial banks’ CRM needs. In addition, Ghanaian commercial banks have not fully adopted some of the recommended strategies that are used to mitigate credit risk associated with agricultural lending. The study unveils some appropriate strategies used to mitigate credit risk exposure in agricultural finance among commercial banks. These strategies include agricultural value-chain financing, collaboration with off-takers, incentive-based and risk-sharing schemes, adoption of a holistic agricultural value chain financing, policy interventions, use of agricultural insurance pool, and the proper structuring of agricultural loans.
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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 | 0.004 | 0.013 |
| 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) | 0.001 | 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".