Ecosystems as an Innovative Tool for the Development of the Financial Sector in the Digital Economy
Bibliographic record
Abstract
The purpose of this article is to analyze the trends in the development of the financial sector, as well as the digital technologies used in this area, to identify the fundamental drivers for improving the ecosystem of the financial sector of the economy. Achieving sustainable business growth is one of the urgent tasks of management, both at the level of individual enterprises and organizations and the national economic system as a whole. This issue is of the highest relevance in the context of the high dynamism of the external environment and the growing level of uncertainty. When writing the article, the following research methods were used: trend analysis, visual graphical analysis, descriptive statistics, correlation-regression analysis, and cross-tabulation. Based on the results of the analysis, it can be concluded that the following indicators have the greatest impact on the ecosystem of the financial sector: the share of financial organizations that had special software for managing the procurement of services; the share of financial organizations that had special software for managing the sales of services. With regards to the Russian financial sector, there is a weakness in the development of the ecosystem, which is partly due to the insufficient use of complex digital solutions in managing financial flows, for example, the use of ERP systems (enterprise resource planning), CRM systems (customer relationship management), and SCM systems (supply chain management). We believe that the conclusions and results presented in this article can be used as methodological tools for developing strategies for improving the ecosystem of the financial sector in the context of the transition to a digital economy.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".