ALTERNATIVE «ONLINE-FINANCING» IN THE REGIONS OF THE WORLD: STATUS AND WAYS OF IMPROVEMENT
Bibliographic record
Abstract
The article examines the scope and structure of the activities of thirteen business models of alternative online finance, by region of the world. The sales volumes for each category of business models are presented, giving an idea of the effectiveness of various types of "online financial services" in different regions of the world. The general properties and features of the global market of alternative financial models show the dominance of the models "Balance Sheet Business Lending", "P2P/Marketplace Consumer Lending", "P2P/Marketplace Business Lending", which by the end of 2024 occupied a market share, respectively: 28.9; 21.7 and 16.7%. These models occupy leading positions in terms of sales, which indicates their popularity and accessibility. A study of the sales volume structure of online financial services by region in terms of financial models shows that two regions have the largest share - USA & Canada and UK, which have an average of 47.8 and 24.3% for 2023-2024, respectively. High rates can be associated with a developed economy, a strong legal system, a high level of investor confidence and the availability of capital. In order to improve the activities of the AOF market participants, it is advisable to carry out the following activities: a) market diversification: it is necessary to encourage the use of a range of models to meet the diverse needs of investors and borrowers; b) increased regulation: improving the regulatory framework to protect investors and market stability; c) technology integration: the introduction of innovative technologies to improve service delivery; d) increased awareness and understanding of alternative financing options and risks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".