Opportunities and Barriers for FinTech in SAARC and ASEAN Countries
Bibliographic record
Abstract
This article assesses the opportunities and challenges for different categories of FinTechs in the SAARC and ASEAN regions. We consider the global financial inclusion data released by the World Bank and map the responses to gain insights into the opportunities and challenges for FinTechs in the respective regions. We develop a new index, termed the FinTech Opportunity Index (FOI), to conceptualise the opportunities and barriers based on individual savings, borrowings, purchasing behaviour, and payment preferences. We note that FinTech services have potential opportunities for expansion in the ASEAN regions but less so in the SAARC regions. The need for different types of FinTech services varies between regions. Services such as crowdfunding, neobanks, and InsurTech have potential in the ASEAN regions, especially with the positive attitude towards entrepreneurship and asset investments. In the SAARC regions, InsurTechs linked to health care has potential along with LendTechs and neobanks. We further note that males, and the young are more likely adopters of FinTechs in both regions. The analysis suggests the need for innovative promotions and education to motivate the more sceptical, especially women and the elderly population, to adopt FinTech services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".