Building Socio-Technical Trust in Kazakhstani Banking Audits Through Estonia’s Digital Governance Model
Bibliographic record
Abstract
The purpose of this study is to develop and substantiate the socio-technical trust architecture (hereinafter – STTA) model, adapted to the national practice of banking audit, drawing on Estonia’s experience and on the theoretical frameworks of socio-technical systems and institutional trust. The research methodology is based on a documentary analysis of Kazakhstan’s regulatory framework, a comparative study of international experiences (in particular, the Estonian X-Road model and KSI blockchain technology), as well as theoretical modelling. The work uses statistical materials from the National Bank of the Republic of Kazakhstan, the Agency for Regulation and Development of the Financial Market (2022-2024), data from international organizations (the World Bank, the OECD), as well as empirical research on the Estonian practice of digital auditing. A four-level STTA model has been developed, comprising the user level (portals for civil audit via NDID), the management level (regulatory sandboxes), the technical level (blockchain audit, API infrastructure), and target trust indicators (Public Verifiability Index, “trust rate” metric). The model assumes an increase in the level of public trust in banking auditing in Kazakhstan to 80% by 2030 (from the current ~38%), a 30% reduction in repeated violations, and a significant decrease in fraudulent transactions. The study highlights the need for regulatory recalibration and IT infrastructure upgrades to build trust through Estonia-inspired mechanisms. The results are practically relevant for transition economies seeking to strengthen digital accountability and citizen engagement in financial oversight.
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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 | 0.002 |
| Open science | 0.001 | 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".