ZOLOZ SMART AML is a risk-based anti-money laundering (AML) compliance platform that unifies watchlist screening, transaction monitoring, and customer risk rating (CRR) into a single solution — reducing manual effort in the AML process by up to 80%.
ZOLOZ SMART AML
ZOLOZ SMART AML is a risk-based AML compliance platform that covers the full anti-money laundering lifecycle. It combines watchlist screening, transaction monitoring, and customer risk rating (CRR) into a single solution — backed by AI-powered detection, regulator-approved compliance models, and integrated case management.
Case Management is integrated across all three modules: screening, transaction monitoring, and customer risk rating.
Screening
Overview
Watchlist screening helps financial institutions manage economic sanctions compliance risk. The system screens customers and transactions against internal and external watchlists, returns matching results, and routes hits to an investigation platform for manual review — all within a single product.
A complete screening program must:
Screen transactions and customers against multiple sanctions lists
Identify politically exposed persons (PEPs)
Prevent transactions involving sanctioned countries, organizations, and individuals, or dealings with embargoed goods
ZOLOZ SMART AML meets these requirements through a score-based screening engine paired with a full investigation and alert management workflow.
Screen engine
The screening engine applies proprietary text-processing rules and fuzzy matching algorithms to screen input data against watchlist records. Each screened record receives a matching score between 0 and 100, where 100 represents an exact match. Only records with a matching score above the pre-configured threshold are surfaced as hits for alert review.
Hits are passed to alert investigators on the built-in case investigation platform for manual review and disposition.
System functions
Real-time screening
The following diagram shows the real-time screening workflow.
Batch screening
The following diagram shows the batch screening workflow.
Dashboard
The real-time dashboard gives compliance teams a consolidated view of screening activity and alert status. It tracks the following metrics:
Today's key indicators: alert volume, match rate, and alert review completion rate
Screening and alert trends: screened volume and alert volume over the past 7, 14, or 30 days
Scenario coverage: total number of active screening scenarios and their percentage breakdown over the past 7, 14, or 30 days
Alert manager
Alert Manager supports the internal compliance review process. It is a web-based application used by analysts, compliance professionals, managers, and administrators throughout the alert investigation workflow. Alert Manager has two main views:
Task List: displays assigned alerts for review and triage
Review Detail: provides the full investigation context for each alert
Transaction monitoring
Monitoring transactions for suspicious activity is a core requirement of any AML program. Financial institutions must maintain systems that generate accurate, timely, and complete information to detect and report suspicious activity.
ZOLOZ SMART AML's Transaction Monitoring module provides flexible rule configuration and a processing infrastructure to support rational risk assessment and efficient risk mitigation.
Overview
The transaction monitoring system screens transactions and customer data on a batch basis, generates alerts for investigation, and integrates with Case Management for alert review and suspicious transaction report (STR) reporting.
The following diagram shows the high-level system architecture.
The following diagram shows the Transaction Monitoring workflow.
Dashboard
The Transaction Monitoring dashboard summarizes key metrics and lets compliance teams monitor money laundering risk at the enterprise level and track investigation progress.
Model manager
Model Manager is where users configure the rules that govern transaction monitoring. It supports two model types:
Transaction Monitoring Model: a collection of rules that runs automatically. Rules with different execution frequencies can be added to the same model, and existing rules can be edited at any time.
Alert Integration Model: a single-rule model that aggregates transactions flagged by the Transaction Monitoring Model and pushes the results to Case Management as alerts for review.
Customer risk rating
Overview
Customer risk rating (CRR) determines the risk level of each customer and triggers enhanced due diligence (EDD) tasks based on pre-configured models.
Real-time customer risk rating
The following diagram shows the real-time CRR workflow.
Periodic customer risk rating
The following diagram shows the periodic CRR workflow.
Dashboard
The CRR dashboard monitors risk class distribution and high-risk customer trends across CRR models.
Model management
Models are the core configuration unit in CRR. Each model applies rules to rate the risk level of a customer. Two execution modes are supported:
Batch mode: runs periodic risk assessments across a customer population
Real-time mode: rates risk as customer events occur
A model can only rate one subject type — either individuals or entities — so separate models are required for each. Before configuring a model, create the appropriate batch factors or real-time variables. For details, see how to create a periodic CRR batch model (section 2.2.1), configure rules in Rule Manager (section 2.2.2), and create and publish a real-time model (Chapter 3).
Key benefits
Advanced screening accuracy
The screening engine combines multi-dimensional fuzzy matching — semantic, phonetic, and text matching — to support inner-language matching across all languages and cross-language matching across more than 15 language pairs, with particular coverage for Asia-Pacific languages. Granular filter configuration lets compliance teams lower the false positive rate by approximately 40%, reducing unnecessary friction for legitimate customers.
Risk-based approach with flexibility
A low-code interface lets compliance teams configure CRR rules and segment customers into risk categories using custom rulesets — without engineering involvement. The resulting risk scores feed directly into downstream risk-based activities.
Network analysis with full explainability
The graph-based network analysis engine detects anomalies from large-scale behavioral and sequential data patterns that rule-based systems cannot handle. Advanced learning capabilities include few-shot learning, weak-label learning, dynamic graph analysis, and group detection. Every detection outcome includes a detailed audit trail that supports regulatory reporting and internal governance.
Out-of-box rules at reduced cost
Configurable tools and granular rule settings automate AML tasks with minimal manual effort. The integrated Case Management portal streamlines case review and STR generation. Industry-specific expert rules give financial institutions a head start without building from scratch. Together, these capabilities cut manual effort in the AML process by up to 80%.
Highly performant and scalable SaaS solution
Tested across more than 30 entities and over 300 business scenarios, the cloud-native SaaS platform handles billions of transactions per day with near-real-time responses. A pay-as-you-go model lowers upfront investment for institutions starting small, while the elastic infrastructure scales to match growth without compromising performance.