How Do Banks Interact with Fintechs? Forms of Alliances and their Impact on Bank Value
Bibliographic record
Abstract
The increasing pervasiveness of technology-driven firms that offer banking services has led to a growing pressure on traditional banks to modernize their core business activities. Banks attempt to confront the challenges of digitalization by cooperating with financial technology firms (fintechs) in various forms. In this paper, we investigate the factors that drive banks to form alliances with fintechs. Furthermore, we analyze whether such bank-fintech alliances affect the market valuation of banks. We provide descriptive evidence on the different forms of alliances occurring in practice. Using hand-collected data covering the largest banks from Canada, France, Germany, and the United Kingdom, we show that banks are significantly more likely to form alliances with fintechs when they pursue a well-defined digital strategy and/or employ a Chief Digital Officer. We evidence that markets react more strongly if digital banks rather than traditional banks announce a bank-fintech alliance. Finally, we find that alliances are most often characterized by a product-related collaboration between the bank and the fintech and that banks most often cooperate with fintechs providing payment services.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".