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
4Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5144246179
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Muhammad Usman Malik (openalex, provider refresh)
- Muhammad Usman Malik (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Regulatory Compliance: 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 |
|---|---|---|---|
| Transformer models that read board networks sharply improve prediction of corporate distress and fraud—AUC rises roughly 12–18 points for distress and 22–28 points for fraud versus conventional baselines; attention analysis highlights independent boards, audit committee expertise and director centrality as resilience signals. The paper pairs these predictive gains with DiD, neural propensity weighting, panel VAR and IVs to support causal claims, but key identification diagnostics and sample details are insufficiently reported.openalex | Muhammad Usman Malik provider id |
2026-08-28 | 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.