Censored Regression Techniques for Credit Scoring: A Case Study for the Commercial Bank of Zimbabwe (Bulawayo)
Bibliographic record
Abstract
Credit creation is the main income generating activity for banks. However this activity involves huge risks to both the lender and the borrower. The risk of a trading partner not fulfilling his or her obligation as per the contract on due date or any time thereafter can greatly jeopardise the smooth functioning of a bank’s business. Credit risk therefore is one of the greatest concerns to most banking authorities and banking regulators. This paper is aimed at coming up with a model that can be used by the Commercial Bank of Zimbabwe in calculating the risk associated with credit scoring. The data set used covered personal loans from January 2010 to January 2012. Linear and Buckley James regression tests were employed to find the explanatory variables influencing time to default and repayment. In investigating customer classification, linear discriminant analysis was applied. Age, marital status, loan purpose and time at current job were found to be linearly related to time to default. Time to repayment was found to be linearly related to age, marital status and loan purpose. 67.5% of the original cases were found to be correctly classified. Buckley James regression out performed linear regression hence it was found to be the most suitable method in determining variables affecting risks in loan lending.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".