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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2422293939
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Zechun Zhu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Ai Safety And Ethics: 1 paper
- Error Rate: 1 paper
- Task Completion Time: 1 paper
- Adoption Rate: 1 paper
- Firm Productivity: 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 |
|---|---|---|---|
| AI is reshaping Chinese finance by automating credit approval, customer service and risk detection, but gains are concentrated in large firms while smaller banks lag and algorithmic bias and fraud risks demand stronger data standards and supervision.openalex | Zechun Zhu provider id |
2026-08-04 | 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.