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:
A5119241097
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Monisola Beauty Ayankoya (openalex, provider refresh)
- Monisola Beauty Ayankoya (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Other: 1 paper
- Labor Share: 1 paper
- Ai Safety And Ethics: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Firm Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| An AI analytics architecture markedly improves SME forecasting and fraud detection in tests: it delivers 31% better 12‑month revenue forecasts than ARIMA and near‑perfect fraud detection (F1=0.947) on evaluated datasets, while cutting reported operational recovery times by about 29% across a 215‑firm sample.openalex | Monisola Beauty Ayankoya provider id |
2026-01-01 | 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.