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:
2344587835
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Mehedi Hasan (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Adoption Rate: 1 paper
- Task Allocation: 1 paper
- Firm Productivity: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Decision Quality: 1 paper
- Employment: 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 |
|---|---|---|---|
| Lenders that more fully deploy AI-enabled financial information systems produce markedly better credit forecasts, and those better forecasts are tied to more consistent approvals, fairer pricing and stronger SME survival and revenue growth. The study finds AI-FIS adoption explains 41% of forecasting variance, and monitoring and approval measures are associated with materially higher odds of SME survival.openalex | Mehedi Hasan provider id |
2026-01-01 | 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.