The Contribution of Robo-Advisors as a Key Factor in Commercial Banks’ Performance After the Global Financial Crisis
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
In several countries, digital financial advisory services, particularly those supported by robo-advisors, are becoming increasingly popular in retail banking. These tools assist users with financial decisions such as risk assessment, portfolio selection, and rebalancing—all at a reduced cost. Recent studies suggest that, over time, robo-advisors could complement human financial advisors. Building on this research, which evaluates robo-advisors’ effectiveness in asset allocation, this study aims to assess the impact of this strategic shift on retail banks’ profitability. It compares the Canadian and French banking sectors, where robo-advisors were introduced in the 2010s. Results indicate that implementing robo-advisors enhances profitability in non-interest activities, with this effect being more pronounced in France than in Canada.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it