Fintechs – grandes institutions financières : comment faciliter le succès de la collaboration pour nourrir l’innovation
Bibliographic record
Abstract
La dynamique de collaboration entre petites start-up du domaine de la finance technologique, communément appelées « fintechs », et grandes institutions financières (GIF) traditionnelles, telles que les banques et les sociétés d’assurances, intrigue. Initialement considérées comme entreprises concurrentes, les fintechs et les institutions financières se retrouvent face à la nécessité de collaborer, en vue d’accélérer l’innovation de technologies financières. En s’appuyant sur trois études de cas de collaborations actuelles entre fintechs et institutions financières au Canada, cet article analyse les facteurs qui leur permettent de mieux collaborer et entretenir leur relation. Dans ce cadre, deux constats sont mis en avant : 1 – la dynamique de la relation se base surtout sur une logique de coopération et non pas de compétition ; 2 – il existe plusieurs mécanismes importants (individuel, structurel, et de processus), sans lesquels la coopération ne peut se développer. Pour finir, cet article offre aux praticiens des recommandations pour réussir leur collaboration.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".