Banking and Digital Transformation: Towards an Integration of Fintechs’ Activities to Develop Innovation
Bibliographic record
Abstract
The emergence of Fintechs, these new entrants whose vocation is to associate digital technologies with finance, is today overturning the business models of traditional banking institutions. While traditional banks are still dependent on existing bureaucratic systems, Fintechs provide new financial solutions enabling customers to adopt new, faster and more flexible ways to manage their finances in a digital environment. Also, given the new competitive environment, banks have realized that Fintechs have a disruptive potential that needs to be integrated in order to maintain a competitive advantage. In this research, we wish to mobilize the theoretical framework of "absorptive capacity" (Cohen and Levinthal, 1990; Zahra and George, 2002) in order to identify and understand the determinants and mechanisms implemented by banks to integrate the skills developed by fintechs. The results show that the integration of these skills could enable banks to keep pace with a changing banking industry by appropriating disruptive services and business models, while leveraging their own strengths.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".