The role of financial technology to increase financial inclusion in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it