Driving Financial Inclusion in Indonesia with Innovative Credit Scoring
Bibliographic record
Abstract
Innovative Credit Scoring (ICS) holds promise for reshaping financial inclusion in Indonesia, offering a potent alternative to conventional credit assessments that often exclude underserved populations. By leveraging alternative data—from telco records to e-commerce and social media footprints—and AI/ML technologies, ICS can deliver more accurate, inclusive, and responsive credit evaluations. However, its potential is constrained by structural inefficiencies and weak regulatory frameworks. This study employs a qualitative, exploratory design based on eight focus group discussions with 36 stakeholders, including regulators, financial institutions, data providers, and academics. Thematic analysis reveals three core barriers: fragmented regulation, limited data interoperability, and algorithmic opacity. To address these challenges, the paper recommends four policy priorities: (1) enforce and expand POJK 29/2024; (2) establish interoperable, integrated MSME data systems; (3) mandate algorithm audits to reduce bias and opacity; and (4) invest in digital infrastructure to close regional access gaps. Without these systemic shifts, ICS may fall short of its inclusive promise.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".