EFFECTS OF RISK MANAGEMENT ON ACCESSING CREDITS OF DEVELOPMENT FINANCE BANKS IN NIGERIA
Bibliographic record
Abstract
The paper investigated the effects of risk management on accessing credits of development finance banks in Nigeria. The study adopted the cross-sectional survey design. The convenience sampling technique was adopted in selecting the 387 respondents. Descriptive and inferential statistical analytical methods were employed for analyzing the data. Structural Equation Modeling (SEM) technique was used in testing the hypotheses developed for this study. The study established that the patronage of credit facility in development finance banks largely depends on credit risk management strategy. Specifically, the volume of credit offers in the banks signified the rate of credit patronage. This was evident that in most cases, that the customers turned down the idea of seeking banks loan if money made available could not help their situations. People approach the banks for loan to develop their commercial activities significantly, and when amounts likely made available by the banks is too small for their needs, the alternate approach was to source money from any available opportunity. In line with findings and conclusion drawn from the study, it is recommended that the managers in the development finance banks in Nigeria should ensure that they took into consideration; credit volume alongside other factors–tenor of facility, terms and other loan repayment technicalities in order to favour the customer patronage and prevention of credit failures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".