AI-Driven Financial Planning And Analysis: From Corporate Strategy To National Policy
Bibliographic record
Abstract
This paper examines the transformative impact of Artificial Intelligence (AI) on Financial Planning and Analysis (FP&A), highlighting its capacity to automate routine processes, deliver advanced predictive insights, and elevate the strategic role of finance professionals. By shifting the focus from transactional tasks to strategic decision-making, AI enhances organizational agility, accuracy, and efficiency. Beyond enterprise-level benefits, the integration of AI into FP&A holds broader implications for national economic policymaking, offering more timely, data-driven insights to inform fiscal and monetary strategies. However, the deployment of AI in FP&A also introduces significant challenges. These include concerns bout data quality and integrity, model transparency and interpretability, cybersecurity vulnerabilities, ethical implications, and the evolving regulatory landscape. In response, the competencies required of FP&A professionals are rapidly changing. Mastery of data analytics, fluency in AI technologies, critical thinking, adaptability, and strong communication skills are becoming essential in navigating this new landscape. This paper contributes to academic discourse by synthesizing recent scholarly findings on AI in finance and presenting empirical insights from a multi-case study approach. By illustrating how AI-driven financial analytics can enhance corporate performance while simultaneously informing macroeconomic policy, it bridges the gap between firm-level financial strategy and national economic planning - an area underexplored in existing literature.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".