The role of financial technology to increase financial inclusion in Indonesia
Bibliographic record
Abstract
The growth of digital technologies has changed the way of doing financial transactions. Even though the transaction value for financial technology in 2018 grew by 24%, the financial inclusion rate in Indonesia is still low, with 64% unbanked. The aim of the study was to analyze the factors of the growing digital technology that influence customer decisions in choosing financial technology services using customer knowledge as the intervening variable. The growing digital technology is measured using social networking, regulatory services, and financial service facilities variables. The sample of this research focused on the microsegment customers located in Java Island. Statistical data are analyzed using Algorithm PLS. Results show that customer decision in choosing financial technology services was strongly influenced by customer knowledge. Customer knowledge was formed from information gathered from the social network, the formal assurance by the government, the financial service facilities, and financial inclusivity. The study recommends a need to educate, promote, and provide adequate information to increase familiarity and literacy rate with regard to financial technology. The study also recommends an urgent clear government regulation to protect the interests of customers and industries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".