Collaborative enhancement of non-MSME credit and optimization of banking idle funds through P2P platforms
Bibliographic record
Abstract
The market share of peer-to-peer (P2P) has shifted from dominating the P2P lending for Micro, Small, and Medium Enterprises (MSME) to non-MSME. Meanwhile, non-MSME credit is an incumbent main market share banking which possibly makes it a complementary or substitution in P2P lending in non-MSME bank credit. Furthermore, optimizing and maintaining liquidity is important due to banks utilizing intermediation functions. The strictness of bank liquidity could determine the management’s response and policy in determining the best timing to utilize either the FinTech from the P2P platform or the customer’s existing funds first. This study aims to assess the empirical findings of the effect of P2P lending on banking credit that is divided between provinces with strict, normal, and lax liquidity. This study uses data from 33 provinces in Indonesia between January 2022 to December 2022. The study approach uses a regression data panel for the data analysis. The results of this study show that P2P lending positively and significantly impacts bank credits of non-MSMEs in provinces with lax bank liquidity. The stricter the banking the lower the compliments of P2P loans against the bank credits of non-MSME. To the author’s knowledge, no existing studies investigate the P2P lending of non-MSME banking credit that also consider the level of strictness of banking liquidity.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".