Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
1Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2127353235
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- S. Zadvornykh (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Skill Acquisition: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Big Data and AI boost efficiency, risk assessment and inclusion across finance, but their unchecked adoption can amplify systemic risk, cyber‑vulnerability and BigTech dependence; coordinated regulation, strong data governance and investment in specialised human capital are required to secure financial stability.openalex | S. Zadvornykh provider id |
2026-03-31 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.