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
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5146919705
ORCID evidence
Observed aliases (2)
- Shu Chen (openalex, provider refresh)
- Shu Chen (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Inequality: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| Public alternative data plus gradient-boosted machine learning sharply improves SME credit-risk prediction compared with traditional scores, giving lenders a more forward-looking and inclusive underwriting tool; gains are promising but hinge on data coverage, interpretability, and external validation.openalex | Shu Chen orcid |
2026-02-03 | 3 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 3 cumulative citations. This is a coverage summary, not an author score or h-index.