Impact of AI Disclosure on the Financial Reporting and Performance as Evidence from US Banks
Bibliographic record
Abstract
Purpose: This study examines the impact of artificial intelligence disclosure within the US banking sector. It may explore the implications of AI disclosure on issues like financial reporting, transparency, accountability, and ethical considerations within the banking sector. Design/methodology/approach: Using a blend of qualitative and quantitative analyses, the researchers utilized SEC and NASDAQ databases to scrutinize AI disclosures within the top 10 banks. The sample comprised 100 annual reports, and through multiple regression analysis, the research discerned a noteworthy enhancement in performance metrics. Findings: The study found that AI influences financial performance only when moderated by the interaction of shareholders, the board of directors, and independent board members. The findings indicate a rising trend of AI disclosure in financial reports. The study indicates that AI disclosure impacts NII, TEXP, and P/E. Additionally, the study indicated a conflict of interest between agents and principals. Large shareholders tended to favor more AI disclosures, whereas the board of directors either did not support or adopted a more conservative stance on disclosure. Research limitations/implications: This study acknowledges a limitation in the dataset; initially comprising 100 annual reports, it was later refined to meet regression analysis assumptions. Despite this limitation, the study’s insightful results contribute significantly to our understanding of the dynamic relationship between AI disclosure and the performance of top-tier banks in the USA. Originality/Value: By investigating the impact of AI disclosure, the study aims to provide insights into the broader considerations associated with artificial intelligence disclosures in the US banking sector. This study also analyzes how stakeholders respond to the disclosed information about artificial intelligence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".