Money Laundering Prevention through Regulatory Technology and Internal Audit Function in Indonesia Banking Sector
Bibliographic record
Abstract
Money laundering poses a significant challenge globally, involving using cash to conceal the origins of funds. With the rise of digitalisation and the adoption of financial technology (FinTech), the financial sector has been compelled to adapt to these changes. The COVID-19 pandemic has further accelerated the use of FinTech services, including digital banking, to address social distancing concerns and enhance customer convenience. Despite having an index of risk considered moderate for money laundering, Indonesia continues to be a destination for these types of illegal activities. Anti-money laundering (AML) and counter-terrorism financing (CFT) programs must be implemented, especially in all financial service providers that Bank Indonesia oversees.Additionally, the internal audit function is crucial in identifying money-laundering activities within banks. However, there needs to be more research regarding integrating RegTech and evaluating internal audit functions in preventing money laundering in Indonesian banks. This paper aims to address this gap by examining the benefits of RegTech solutions and the role of internal audit functions in preventing money laundering. The findings can be used to enhance regulations and implement effective measurement to combat money laundering and illicit activitivities. Furthermore, the study highlights the importance of Indonesia's membership in the Financial Action Task Force (FATF) to strengthen the country's AML framework and contribute to global policies on AML and countering the financing of terrorism.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".