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Execution Platform

AML Verification

Unified data, services, workflows and agents in one operating substrate.

AML Verification is part of the Advanze platform layer: the execution foundation that enables applications, services and AI agents to work as one operating system for the business. It is designed for extensibility, governance and scale from the start.

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Professionals reviewing onboarding evidence and compliance-sensitive customer work.
AML Verification starts with people trying to make the right call. A new customer needs to be onboarded quickly.

Execution Platform / AML Verification

Built as an execution foundation

Advanze is positioned around the idea that enterprise software must evolve from passive systems of record into active systems of execution. In that model, this page is not just a feature description. It explains how AML Verification contributes to an operating environment where people define intent, agents execute governed work, and leadership can see progress through unified data.

The value is strongest when the capability is connected to adjacent processes. Records, workflows, controls, communications and analytics should not live in separate tools. They should participate in a shared execution fabric that can coordinate work across departments while preserving human accountability.

Professionals reviewing onboarding evidence and compliance-sensitive customer work.
Where AML Verification becomes real work people can trust. People reviewing onboarding, evidence and compliance-sensitive customer work with care and control.

Core capabilities

What AML Verification enables

Each capability is designed to work as part of the broader execution platform rather than as a disconnected module.

Sanctions List Screening

Screen customers against OFAC, UN, EU, and global sanctions lists in real-time. Agents automatically check names, dates of birth, and addresses during onboarding and ongoing monitoring.

PEP Detection

Identify politically exposed persons using curated databases of government officials, executives, and their close associates. Agents apply enhanced due diligence workflows for high-risk profiles.

Adverse Media Monitoring

Continuous scanning of news sources for negative mentions related to financial crime, corruption, or reputational risk. Agents alert compliance teams when customers appear in adverse media.

Risk Scoring

AI-driven risk assessment combining sanctions, PEP status, adverse media, transaction patterns, and behavioral signals. Agents assign risk levels that drive differentiated monitoring intensity.

Ongoing Screening

Automated re-screening of existing customers against updated watchlists and adverse media. Agents detect status changes and trigger reviews without manual batch processes.

Audit-Ready Reporting

Comprehensive audit trails with timestamped screening results, data sources, and decision rationale. Support regulatory examinations and internal compliance reviews with complete documentation.

The Advanze Control Model - AI Agents Execute Work Inside the Control Model

Agentic operating model

AI agents execute work inside the control model

Advanze treats AI agents as active participants in the execution model rather than passive assistants. Agents can autonomously read context from unified data stores, call platform services to perform operations, update business records in real-time, trigger multi-step workflows, prepare decision packages for human approval, and escalate exceptions when policies require oversight. Human teams define the guardrails and maintain accountability for business outcomes.

This architecture matters because it transforms work execution across the enterprise. Instead of adding chatbots on top of disconnected systems, Advanze provides an execution substrate where agents operate with consistent permissions, follow the same governance policies as human users, generate complete audit trails for every action, and share unified data visibility with human colleagues. Work moves faster while control strengthens.

Business outcomes

What becomes possible

Scale by designArchitecture supports growing workloads, data and agent activity.
Governed automationSecurity, identity and controls are embedded in execution paths.
Composable servicesTeams can build faster using reusable platform capabilities.
Operational confidenceTelemetry and auditability make execution observable.
Professionals reviewing onboarding evidence and compliance-sensitive customer work.
The outcome is not just automation. It is confidence in what happens next. When AML Verification runs inside a governed execution model, teams can move faster without losing judgement, accountability or trust.

Agentic use case

Where AML Verification becomes governed execution.

A new customer needs to be onboarded quickly.

What makes it harder in the real world: Onboarding often crosses identity verification, document collection, AML, sanctions, company registry checks, credit, legal terms, customer data quality, workflow approvals and audit evidence.

What Advanze changes: Coordinate onboarding, KYC checks, document review, risk scoring, approvals and customer communication through a governed agentic workflow.

Onboarding AgentGuides the customer journey, checks missing information and coordinates next steps.
KYC AgentRuns identity, address, company, tax, biometric, credit, AML, PEP and sanctions checks where applicable.
Document AgentExtracts, classifies and validates submitted documents.
Risk AgentCombines verification results with risk policies and flags escalations.
Compliance ReviewerApproves medium and high-risk cases or requests additional evidence.
Challenge

Onboarding often crosses identity verification, document collection, AML, sanctions, company registry checks, credit, legal terms, customer data quality, workflow approvals and audit evidence.

Orchestration

Coordinate onboarding, KYC checks, document review, risk scoring, approvals and customer communication through a governed agentic workflow.

Success

Coordinate onboarding, KYC checks, document review, risk scoring, approvals and customer communication through a governed agentic workflow.

Related execution patterns

Complex products usually touch more than one governed workflow.

GRC, Security, Operations

Incident Response with Governed Escalation
Coordinate incident intake, severity classification, evidence collection, stakeholder notification, escalation and remediation tasks through governed agentic workflow.

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Legal, Procurement, Sales, Compliance

Contract Review and Obligation Routing
Review contracts for obligations, risks, pricing terms, renewal dates, data clauses and approval requirements before signature or operational handoff.

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DataServices, Data Governance, Operations

Data Quality Exception and Point-in-Time Recovery
Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.

Explore pattern
Professionals reviewing onboarding evidence and compliance-sensitive customer work.
Onboarding review 4People reviewing onboarding, evidence and compliance-sensitive customer work with care and control.
Professionals reviewing onboarding evidence and compliance-sensitive customer work.
Onboarding review 5People reviewing onboarding, evidence and compliance-sensitive customer work with care and control.
Professionals reviewing onboarding evidence and compliance-sensitive customer work.
Onboarding review 6People reviewing onboarding, evidence and compliance-sensitive customer work with care and control.

Why AI execution needs architecture

The work needs context, controls and clear permissions before automation can safely act.

That is why the Advanze control model matters: identity, permissions, policies, workflow, audit evidence and human judgement are embedded into execution before agents act.

  • Onboarding often crosses identity verification, document collection, AML, sanctions, company registry checks, credit, legal terms, customer data quality, workflow approvals and audit evidence.
  • The right agent must receive the right context, tools, permissions and approval path before work moves forward.
  • Audit evidence, exception handling and human judgement need to be part of the workflow, not notes added after the fact.
View related pricing Explore Multi-Agent Platform

Implementation path

How to move from concept to production

Advanze can be introduced progressively. The recommended path is to start with a visible workflow, prove the operating model, then expand into adjacent capabilities as the platform foundation matures.

  1. 1
    Assess the current operating model

    Map the workflows, systems, data sources and manual coordination points around this capability.

  2. 2
    Design the target execution flow

    Define the data model, human approvals, agent tasks, service calls and governance controls.

  3. 3
    Launch a focused implementation wave

    Start with a bounded use case that proves the operating pattern and creates reusable platform assets.

  4. 4
    Scale across the business

    Extend the pattern to adjacent workflows, more agents, more users and deeper integrations.

Next step

Build this into your execution platform roadmap

Explore how AML Verification can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.

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