Model of Optimizing Correspondence Risk-Return Marketing for Short-Term Lending
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
The modern credit market is actively changing under the influence of digitalization processes. Some of the drivers of these changes are financial companies that carry out, among other things, online lending. Online lending is objectively focused on short-term small loans, both payday loans (PDL) and short-term loans for SMEs. In our research, we applied a special segmentation of borrowers based on the whale-curve approach. Such segmentation leads to four segments of borrowers (A, B, C, and D) which are characterized by the specific features of profitability, risk, recurrent loan granting, and others. The model of optimal correspondence between “risk–return-marketing efforts” is elaborated in the mentioned segments. Marketing efforts are considered in the context of the optimization of the marketing-budget allocation. Our approach was essentially grounded in special scoring-tools that allow multi-layer assessment. A scheme of assessment of profitability, risk, and marketing-resources allocation for borrower’s inflow is constructed. The results can be applied to the customer relationship management (CRM) of online non-banking lenders.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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