Cognitive Computing and Business Intelligence Applications in Accounting, Finance and Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cognitive computing encompasses computing tools and methods that simulate and mimic the process of human thinking, without human supervision. Deep neural network architectures, natural language processing, big data tools, and self-learning tools based on pattern recognition have been widely deployed to solve highly complex problems. Business intelligence enhances collaboration among different organizational departments with data-driven conversations and provides an organization with meaningful data interpretation for making strategic decisions on time. Since the introduction of ChatGPT in November 2022, the tremendous impacts of using Large Language Models have been rippling through cognitive computing, business intelligence, and their applications in accounting, finance, and management. Unlike other recent reviews in related areas, this review focuses precisely on the cognitive computing perspective, with frontier applications in accounting, finance, and management. Some current limitations and future directions of cognitive computing are also discussed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 it