RegTech and Blockchain Integration in AML Compliance: Financial and Operational Impacts
Notice bibliographique
Résumé
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 Due to challenges such as outdated systems, legal uncertainty and fragmented regulation, wider adoption and scalability are still hindered.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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».