Transformative Impact of Artificial Intelligence and Blockchain on the Accounting Profession
Bibliographic record
Abstract
This research paper uses qualitative analysis to examine the profound influence of artificial intelligence (AI) and blockchain technologies on accounting practices. The study utilizes case studies and semi-structured interviews with industry experts to identify central themes, including efficiency and automation, accuracy and data integrity, fraud detection and security, professional roles and skills, and ethical and regulatory considerations. The results demonstrate that AI increases efficiency by automating repetitive tasks and enhancing fraud detection, while blockchain guarantees the precision and reliability of financial records. Nevertheless, incorporating these technologies into existing systems poses difficulties, including technical obstacles, adherence to regulatory requirements, and ethical considerations such as safeguarding data privacy and addressing algorithmic bias. Due to these findings, accounting professionals must acquire new skills in data analytics and technology management. It is recommended that educators integrate artificial intelligence (AI) and blockchain into accounting curricula. At the same time, policymakers are advised to establish well-defined regulatory frameworks to facilitate the adoption of these technologies. The study also identifies areas for future investigation, such as the enduring effects of AI and blockchain on accounting methods, the factors that influence user adoption, and the creation of efficient regulatory structures. The research thoroughly analyzes how AI and blockchain are transforming the accounting profession, providing insights into the opportunities and challenges they bring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".