Chronic Shortage of CPAs: Leveraging Robotic Process Automation (RPA) Technology as a Sustainable Solution
Bibliographic record
Abstract
Although the chronic shortage of certified public accountants (CPAs) is a global issue (including in Canada), in the United States the shortage is so acute that many organizations are unable to provide audited financial statements to stakeholders (i.e., tax authorities, regulators, rating agencies, creditors) on time. This crisis has led the American Institute of Certified Public Accountants to create the National Pipeline Advisory Group (NPAG) with the mandate to understand the root causes of the issue and make actionable recommendations. The NPAG made multiple recommendations to increase the CPA pipeline, and its 22-member group recognized that these recommendations alone will not fix the shortage of CPAs; therefore, it encouraged the profession to explore other avenues. Through this review I showed that widespread adoption of robotic process automation (RPA) technology is a sustainable solution to the shortage of CPAs. As research has shown, there are proven use cases, such as an RPA that can complete a task in 17 seconds instead of 17 hours, with an increase in accuracy by 99% instead of 90% frees employees for high-value tasks that require professional judgment. However, there are multiple roadblocks that are preventing the profession from leveraging RPA technology. Removing these impediments will help accelerate the adoption of the RPA technology, address the chronic shortage of CPAs, and contribute to the creation of career opportunities for professional CPAs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".