RISK GOVERNANCE EFFECTIVENESS IN INDONESIA’S ANTI-SCAM ENFORCEMENT: AN EMPIRICAL ASSESSMENT FOR 2025
Bibliographic record
Abstract
This study evaluates the effectiveness of Indonesia’s Financial Services Authority (OJK) in responding to the rising prevalence of financial scams during the first quarter of 2025, with particular emphasis on risk governance and systemic resilience. The regression results (Y = 4194.91 + 14.88X; R² = 0.954; p = 0.023) indicate a strong and statistically significant relationship between the value of reported financial losses and the number of blocked accounts, reflecting OJK’s active and measurable enforcement actions. However, further analysis uncovers a critical paradox: although the absolute number of blocked funds increased, the Fund Blocking Success Rate declined markedly from 5.57% to 2.70%, signalling limited mitigation effectiveness. A subsequent regression examining the relationship between the number of blocked accounts and the fund-blocking success rate revealed weak significance (R² = 0.555; p = 0.255), suggesting structural and systemic disconnections within existing enforcement mechanisms. The novelty of this study lies in demonstrating that real-time enforcement capacity continues to lag behind the rapid escalation of digital scams. This gap is driven primarily by non-interoperable digital infrastructures, insufficient predictive analytical tools, and fragmented institutional coordination. The findings underscore urgent implications: achieving effective risk governance requires the integration of predictive analytics, early-warning systems, and fully interoperable institutional frameworks. By providing original empirical evidence, this research contributes to the regulatory literature and calls for a strategic redesign of Indonesia’s digital financial crime prevention architecture.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".