Fintech and the Future of the Payment Landscape: The Mobile Wallet Ecosystem - A Challenge for Retail Banks?
Bibliographic record
Abstract
Technological innovation, recent regulatory initiatives and mass consumers’ changing expectations are quickly re-shaping the payments’ sector, paving the way to a more open environment where even non-banking players see a huge opportunity to gain momentum and disrupt the incumbents, namely the financial institutions. Fintech startups, high-tech firms but also mobile network operators are indeed challenging the status quo with their innovative propositions, trying to disintermediate banks from their traditional function of payment service providers. In the payments market, mobile wallets represent one of the innovations with highest potential of growth in the consumer-to-business segment. Payment market is a large and profitable segment for retail banking. Besides revenue streams from card payment transactions, new sources of revenueas and value creation have been unleashed by digital payments. This paper contributes to provide a better understanding of the mobile wallet ecosystem, also analyzing a set of four business cases so to identify potential sources of competitive advantage for retail banks in a market characterized by an increased non-bank competition. Mobile wallet platforms can be a powerful tool for banks to cope with the customer-centric approach. The structure of the paper analyse the recent trends in the financial services industry, involving the entry of new players (Fintech); the evolution of payments in the market; the concept of ecosystem applied to the new payment landscape; and it outlines the banks’ roles in the new mobile payment environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".