Marketplace lending of small‐ and medium‐sized enterprises
Bibliographic record
Abstract
Abstract Research Summary When evaluating Internet‐based loan project of small and medium size enterprises (SME), lenders can rely on easy‐to‐understand risk ratings or more sophisticated financial information. We investigate lenders decisions and its effect on loan funding success on the marketplace‐lending platform Zencap. The data set has been provided by the platform Zencap and includes 414 SME marketplace loans and 2,196 lenders. The data examined provide strong support for the importance of simple platform ratings in influencing investor behavior, while the effect of more detailed financial information is less pronounced, controlling for relevant variables. Higher interest rates appear more profitable to investors without any serious concern about non‐repayment. Managerial Summary Platform managers and potential borrowers are interested in how to encourage lenders to pledge their money on marketplace‐lending platforms. We investigate the influence of easy‐to‐understand risk ratings and more sophisticated financial information on investment decisions and their effect on loan funding success on the marketplace‐lending platform Zencap. We investigate 414 SME marketplace loans and 2,196 lenders. We find that for marketplace lending on Zencap the effect of more detailed financial information is less pronounced than easy‐to‐understand risk ratings. Platforms base risk ratings on information other than the entrepreneurs' financial information.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".