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:
A5114871820
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Haresh Barot (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Output Quality: 1 paper
- Governance And Regulation: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Regulatory Compliance: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| A proactive AI-and-blockchain framework for forensic accounting cuts detection times from 47 days post-event to as early as 9 days pre-event and boosts accuracy to over 89%, while adopters report 58% faster compliance resolution, 47% fewer misstatements and an 83% average ROI. These results are promising but stem from a small, India-focused pilot without randomized controls, so wider replication is needed.openalex | Haresh Barot provider id |
2026-05-12 | 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.