Model of Optimizing Correspondence Risk-Return Marketing for Short-Term Lending
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".