The FinTech Skills Gap: Identifying Skills Desired by Bank Employers and Skills Taught in Undergraduate Business/Accounting Programs in Ontario
Bibliographic record
Abstract
This study examined the FinTech skills gap in Ontario with the goal of identifying opportunities for developing FinTech talent in undergraduate business/accounting programs offered at Ontario universities. A literature review revealed a global phenomenon of technology-related skills shortages in the finance industry from the perspective of employers. Although educators do incorporate technology and data analysis tools in the classroom, students are not perceived as being fully proficient in them (Boulianne, 2016; Pan & Seow, 2016; Rackliffe and Ragland, 2016; Sledgianowski, Hirsch, & Gomaa, 2016; Wymbs, 2016). The methodology used in this research involved using text analytics to look at FinTech job postings data from Indeed.com compared against undergraduate program data from the official academic calendars posted on the websites of 19 universities in Ontario. Results reveal with statistical significance that business/accounting educators have a weak level of agreement with bank employers on the hard skills that are most relevant in the industry.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".