What it does.
From a minimal input (an entity name or other identifying information), LUMA autonomously screens legal and natural persons across sanctions, PEP, adverse media, reputational and enforcement risk.
It performs real-time, multi-dimensional contextual analysis across multiple languages, going beyond static watchlists and name matching to identify, verify and assess relevant risk signals.
Each signal is evaluated in context, with complete source attribution and transparent matching criteria, reducing false positives while surfacing risks that traditional list-based screening may miss.
LUMA supports both onboarding and ongoing monitoring, continuously detecting and interpreting material changes in an entity’s risk profile and producing fully documented, regulatory-grade screening results. Every assessment is transparent, traceable and suitable for audit and regulatory review.
How it works.
LUMA sits on CleverChain’s multi-layer intelligence engine, configured specifically for autonomous screening and continuous monitoring.
First, the user’s policies, risk appetite, screening requirements, regulatory mapping and internal data are pre-wired into the workflow.
Second, selected data sources are orchestrated by scenario across sanctions, PEP, enforcement, adverse media, reputational and other relevant risk sources, with no dependency on any single provider.
Third, an LLM ensemble selects the best-fit model for each task based on current performance, rather than relying on a single fixed model.
The engine is policy-aware, scenario-tuned and evidence-generating. LUMA applies contextual analysis to identify, verify and interpret risk signals rather than simply returning potential matches.
Screening within LUMA goes beyond traditional name-against-list matching. Sanctions, PEP, enforcement, adverse media and reputational signals are evaluated against relevant context, including geography, sector, role, chronology and network. Multi-attribute entity resolution across languages, scripts and transliterations helps distinguish genuine risk from false positives and identify relevant signals that static watchlists may miss.
The result is transparent, fully attributable and regulatory-grade screening, with the evidence, sources and matching criteria behind each assessment clearly documented. LUMA applies this intelligence both at onboarding and continuously, reassessing entities as their risk profile changes over time.