Using Environmental and Social Information in Lending Decisions
Bibliographic record
Abstract
There is no doubt that collecting and analysing information is the key element in the process of decision making. Lending decisions, taken by banks, are not exception. In order to ensure that lending decisions are serving banks' goals, the process of taking such decisions involves, inter alia, gathering and analysing information about the prospective and actual clients, who are seeking loans. Such information is mainly related to the financial performance of banks' clients. The recent trend of considering information other than financial one, particularly in developed countries, seems to be basically enforced, rather than promoted, by power of the law. This can be noted in the increasing interest of banks in environmental information, while social information is still, to some extent, far from the attention of such banks. Other factors, such as religious instructions are suggested to play a role in encouraging banks to consider social information. In the case of developing countries, social and environmental information alike seems to be out of banks attention due to many factors including the absence of related laws and the weakness of desire and capacity for enforcing such laws in case of their existence. This article tries to provide more explanation for these points.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".