Evaluating Nonprice Terms to Ration Microfinance Loans Based on Expected Loan Loss Function
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microfinance institutions (MFIs) play a unique role in the financial sector, using an alternative financial intermediation system (business model) to provide banking services to the marginalized. This is particularly important in areas where collateral‐based conventional banking could be more effective. Thus, access to financial services, particularly microfinance loans, is crucial for developing small and medium‐sized enterprises (SMEs), especially in rural areas where traditional banking services may be inaccessible. The objectives of this study are to investigate the extent to which factors other than interest rates impact microfinance loan allocation, evaluate the acceptable level of expected loan loss (ELL) that banks can tolerate without compromising financial stability, and explore how banks strategically allocate assets to risky loans under uncertain market conditions. The results from the ELL function indicated that varying risk profiles significantly influenced sensitivity to changes in loan size. This, in turn, affected the institution’s risk sensitivity and tolerance levels at each branch or with each loan product, thereby aiding in the appropriate loan allocation. The recommendations based on the studies include using nonprice terms, loan evaluations, and strengthening branch‐level decision‐making by empowering branch managers with the necessary tools and training to make decisions that reflect the local context and specific loan products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it