A Delphi Study Investigating the Development of the Moroccan Fintech Ecosystem: Key Challenges and Opportunities
Bibliographic record
Abstract
As Morocco aspires to position itself as a regional hub for financial innovation in Africa, its Fintech sector presents a paradox: despite a robust digital infrastructure and growing institutional support, adoption remains limited. Systemic barriers—such as a persistent cash-based culture, low mobile money usage, and fragmented collaboration—continue to impede the sector’s growth. Against this backdrop, this study applies the Delphi research method to systematically identify and prioritize the most pressing challenges and strategic actions facing Morocco’s Fintech ecosystem. Drawing on the insights of 45 experts from finance, technology, academia, startups, and service-oriented organizations, the study follows a three-phase process: open-ended brainstorming, narrowing down, and final ranking. The process produced consensus around 12 key challenges and 12 strategic actions, including the need for an open banking framework, a unified national Fintech vision, regulatory sandboxes, and improved collaboration between incumbents and startups. These findings offer actionable insights to Moroccan policymakers and industry leaders and contribute to Fintech research in emerging economies.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".