A real-time risk assessment model for cross-border financial transactions based on big data technology
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
Abstract The study applies the method of resampling to deal with unbalanced financial transaction data, which is resampled by the method of majority class weighted minority class oversampling. After data processing, the VaR-GARCH financial transaction risk assessment model is constructed. The financial transaction risk assessment method of this paper is compared with other risk assessment methods to test its assessment performance. Subsequently, taking the carbon financial market as an entry point, the trading price data of seven global carbon financial markets from 2021 to June 28, 2024, are selected for the study to assess the risk of the carbon transnational trading market in real-time. The risk assessment efficacy of this paper’s risk assessment model on both the AP and LC datasets has an overall advantage. Among the seven global carbon markets, the EU has the most drastic fluctuation in transaction prices, while the Chinese carbon market is the smoothest. The transaction price averages from highest to lowest are California-Quebec (85.59), South Korea (72.49), U.S. Regional Greenhouse Gas Emission Reduction Program (47.24), U.K. (44.80), China (37.26), New Zealand (34.35), and EU (34.34). California-Quebec had the highest average transaction price, while the EU had the lowest average transaction price. Transaction prices in China are the most stable, and log yield trends in the UK and South Korea are similar. The top three markets in terms of value-at-risk VaR are California-Quebec, South Korea, and the EU, and the smallest is the UK market.
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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 | 0.001 | 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