Screening AI
Screening AI investigates sanctions, PEP and adverse-media alerts end to end — resolving false positives automatically and escalating genuine risk with the evidence and rationale analysts need
Up to 91%
Alerts auto-resolved
Clear false positives automatically without analyst review
Evidence-led
Every match investigated
Evaluate identity, geography, age, source evidence and contextual risk before making a decision
Traceable
Built for audit
Every decision links back to its evidence, rationale and agent version
Trusted BY LEADING
regulated institutions
The problem
Legacy screening matches names. It doesn't understand them.
Rule-based tools compare strings against static lists, so a common name or a stale alias triggers the same alert as a genuine sanctions hit. The queue fills with false positives, and analysts spend the day clearing noise instead of investigating the few alerts that matter.
The Arva approach
We score risk on evidence — and we can prove the score.
Screening AI is built and evaluated in our research lab against analyst-labelled alert outcomes. It learns what a real match looks like in context — transliterations, aliases, jurisdiction, identifiers — and it is benchmarked continuously, so you can see, and audit, exactly why each alert was cleared or escalated.
Prove it's correct. Then automate it.
Features
A clearer view of every company & entity
Capabilities
Built to decide, not just to flag.
The same evaluation discipline sits behind every Arva agent. KYB AI and KYC AI apply it to the onboarding workflow — turning fragmented data into decisions you can defend.
Website & Web-Presence Analysis
Context-aware name matching — understands transliterations, aliases, common names and jurisdiction, so irrelevant matches are filtered before they reach a human.
Enrichment with Arva Intel
Enrichment with Arva Intel — each alert arrives with the open-source and third-party context needed to disposition it, not just the raw hit.
Runs on your existing stack
Integrates with ComplyAdvantage, LexisNexis and your current providers; no rip-and-replace.
Evidence-backed dispositions
Every clear or escalate is attached to the data and model version behind it.
How we know it's right
Model risk governance, built into every agent.
The same evaluation discipline sits behind every Arva agent. It is why regulated institutions trust the decisions — and can defend them.
Evidence Lineage
Retain every source, key fact, and decision input for review and audit.
Consistent Evaluation
Apply the same defined review logic across alerts, analysts, and teams.
Human Review Controls
Keep analysts in control of escalation, disposition, and final approval.
Model Oversight
Make performance, reasoning, and changes visible to compliance and model-risk owners.
Stacks
Works with the screening stack you already run.
Screening AI works alongside your existing screening providers and case tools, adding investigation and decision support without forcing a platform migration.
