Enhancing Financial Inclusion in ASEAN: Identifying the Best Growth Markets for Fintech
Bibliographic record
Abstract
While most of the advanced economies are facing saturated markets, the Association of Southeast Asian Nations (ASEAN) has been touted a stable and attractive investment region averaging 5.4% growth since 1980. In 2013, ASEAN overtook China as the top foreign direct investment destination. Boasting the world’s fifth largest economy with over 650 million people and 400 million reaching middle class, ASEAN has commendably transitioned from a subsistence economy to product and service industries. Despite the success, many live in marginalized areas without access to banking facilities. Advancing internet capability and availability present investors an opportunity to offer financial technology, or Fintech, to meet the need for financial services in this digital era. The aim of this research is to identify the countries with the highest need for financial inclusion and, hence, the best potential for Fintech growth. The results may help governments formulate policy that improves investment competitiveness. The methodology includes identifying relevant criteria and allocating weight to each criterion to evaluate the best international markets. The findings show Vietnam, Laos, and Cambodia as the countries with the highest potential. The associated risks and opportunities are discussed, followed by managerial implications, limitations, and recommendations for future research.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".