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
2Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5100579186
ORCID evidence
Observed aliases (2)
- Yafei Xu (openalex, provider refresh)
- Yafei Xu (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
Claim outcomes
- Regulatory Compliance: 1 paper
- Error Rate: 1 paper
- 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 |
|---|---|---|---|
| A multimodal deep-learning model merging prior-year raw financial statements, ratios and non-financial indicators markedly improves early detection of accounting fraud among Chinese listed firms from 2010–2023. Trained with a strict temporal design to avoid look-ahead bias, the approach outperforms standard fraud predictors and could help regulators and investors detect fraud sooner — though quantitative metrics, robustness checks and cross-country validity are not reported in the summary.openalex | Yafei Xu provider id |
2026-09-19 | 0 |
Citation observation summary
OpenAlex 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.