Foreign Experience in Digitalization of Preventive Public Financial Control (on the Example of USA, China, Canada, India and Australia)
Bibliographic record
Abstract
The article discusses the issues of implementing preventive financial control using modern digital technologies. The up-to-date level of informatization of the government agencies activity and local governments allows moving to a qualitatively new level of interaction between regulatory authorities and the environment under control. In this case the verification and analysis of reporting is performed not only after the fact of fulfillment but also in a preventative manner. This allows a quick respond to emerging risk situations, prevention of erroneous actions, and management decisions in a real time. When solving the problem of digitalization of state financial control procedures, the emphasis and priorities can be placed differently, both in the selection of automated processes and in the selection of technological solutions. The experience of other countries represents a valuable source of knowledge in this area. Therefore, the aim of the article is to study the best practices in digitalization of state financial control and to develop the recommendations for implementing preventive digital state financial control in Russia. This article presents the analysis of the experience of the USA, China, Canada, India, and Australia. The main projects are considered that were implemented in these countries to improve the financial control by implementing digital solutions. The features and areas of application of cloud computing, technologies of data collection, processing and analyzing the Big Data, as well as systems based on artificial intelligence in the field of public financial control are presented. As a result, recommendations have been formulated for adapting the best practices of these countries for the implementation of preventive digital control in the financial and budgetary sphere in our country. A further elaborated investigation of organization of the collection and processing of data on the object under control, coming not only from its corporate sources and government information systems but also from publicly available Internet resources, is considered as a relevant issue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".