RegTech and Blockchain Integration in AML Compliance: Financial and Operational Impacts
Bibliographic record
Abstract
Background: Anti-money laundering (AML) is hindered by labour-intensive processes and human error, which is detrimental in today’s fast-paced environment. It focuses on how these technologies help boost the core AML function, such as Know Your Customer (KYC), transaction monitoring and compliance reporting, while taking into consideration their financial and operational effect. The use of blockchain technology considerably increases the transparency and security of financial transactions as well as AML compliance, as blockchain is based on a decentralised, immutable infrastructure. Aims: This paper is about investigating the impact of the combination of Regulatory Technology (RegTech) and blockchain in Anti-Money Laundering (AML) compliance in financial institutions. Methodology: A systematic literature review methodology was used, and 12 case-based and empirical studies from a pool of 180 sources were analysed. The study draws on case studies from diverse global jurisdictions, highlighting the role of public-private collaboration in achieving scalable outcomes. Additionally, it emphasised peer-reviewed articles as well as institutional reports with real-world insights on RegTech and blockchain solutions, financial, operational and regulatory performance, in the AML context, across different global jurisdictions. Result and Discussion: The findings indicate that RegTech improves the accuracy during KYC and transaction monitoring, while Blockchain creates transparency and accountability. Compliance costs were reduced and more operational efficiency was reported in most case studies with collaborators of the regulators and institutions, where collaboration between the regulators and institutions existed. Due to challenges such as outdated systems, legal uncertainty and fragmented regulation, wider adoption and scalability are still hindered. Although it is clear that integration of the RegTech and blockchain shows great potential, its full potential can only be realised where there are harmonised global regulations and infrastructure support. Conclusion: Regulatory Technology (RegTech) and blockchain technology become powerful tools that modernise AML (anti-money laundering) compliance, however, only when cohesive regulatory edges, capacity building and investment in technological infrastructure are incorporated. Recommendations for Future Research: Future research should focus on longitudinal case studies having access to internal compliance data, examine a unified RegTech blockchains ecosystem and quantify the amount of harmonisation of global regulations to enable scalable and economically feasible innovation of AML.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".