Regulatory Technology in Digital Customer Onboarding: Mapping Risk-Based Anti-Money-Laundering Controls and Supervisory Effectiveness

Digital customer onboarding has become a critical point at which financial intermediation, technological innovation and anti-money-laundering risk management converge. Regulatory technology can improve identity verification, customer-risk classification, screening and monitoring, yet successful electronic know-your-customer procedures do not necessarily demonstrate effective risk-based compliance. This study examines how the academic literature has conceptualised and evaluated the use of regulatory technology to operationalise risk-based anti-money-laundering and counter-terrorist-financing controls in digital onboarding. A hybrid, sequential evidence-synthesis design combines bibliometric mapping with a targeted structured full-text synthesis. Five modular searches were conducted in the Web of Science Core Collection. The central search intersection generated 39 records; after title-and-abstract screening, 32 records formed the bibliometric corpus. Seventeen strategically selected studies were coded across nine dimensions covering identity proofing, identity assurance, customer money-laundering and terrorist-financing risk assessment, proportionality, regulatory-technology function, governance and auditability, outcomes, supervisory visibility and international cooperation. The results show rapid growth since 2023 but persistent fragmentation across digital identity, blockchain, artificial intelligence, customer due diligence and transaction-monitoring research. Identity proofing is comparatively well developed, whereas the conversion of verified identity data into transparent, updateable and challengeable customer-risk classifications is less consistently specified. The available empirical evidence also indicates that transaction monitoring and dynamic customer-risk modelling produce clearer measurable effects than electronic know-your-customer controls alone. The main unresolved issue is supervisor-readability: whether automated controls generate evidence that authorities can access, understand, challenge and use for intervention and remediation. The study proposes an institution-to-supervisor framework linking identity assurance, risk classification, proportionate controls, model governance, auditable outcomes and privacy-preserving cooperation. The framework provides a finance-oriented basis for evaluating regulatory-technology effectiveness beyond cost reduction and procedural compliance.