If you’re a bank operating across multiple countries and geographies, fraud management can be challenging. Issues like fragmented monitoring systems, differing regulatory requirements, and limited visibility across entities can cause complications in implementing new systems, but without strong fraud management, you’re leaving yourself and your customers at risk.
This article explores solutions to these challenges through multi-tenant fraud management platforms. We’re covering Vyntra, DataVisor, NICE Actimize, and Featurespace.
Comparing top multi-tenant fraud management platforms
Platform | Ideal for | Key multi-tenancy features | Primary capabilities |
Vyntra | Tier 1 and Tier 2 global banking groups, payment processors | Three-level data segregation, shared risk engine, role-based access | Real-time payment fraud, internal fraud, AML, sanctions screening |
DataVisor | Sponsor banks, fintech platforms, payment processors | Parent-child hierarchy, tenant-level overrides, white-label UI | AI-native fraud detection, automated investigations, graph analysis |
NICE Actimize | Enterprise banking institutions, multi-regional banks | Multi-tenant cloud infrastructure, configurable multi-entity workflows | Cross-channel analytics, entity risk scoring, employee fraud monitoring |
Featurespace | Issuers, acquirers, multi-brand banking groups | Single-instance multi-tenancy, analytical configuration groups | Adaptive behavioural analytics, dynamic anomaly detection |
Core capabilities to look for in a multi-tenant platform
Before exploring the solutions themselves, it’s important to first know what you are exactly looking for. When evaluating a multi-tenant platform, we recommend selecting a platform that offers the following:
- Tenant data separation: The platform must keep each entity’s data separate and control access to meet local privacy requirements.
- User access controls: Central risk teams need a group-wide view, while local staff can only access data for their assigned entity.
- Rules and machine learning: Combining business rules with machine learning helps detect known fraud patterns and unusual transaction activity.
- Cross-tenant fraud detection: Analysing payment data across entities helps uncover linked money mule accounts while protecting sensitive customer information.
- Real-time fraud scoring: The platform must score transactions within milliseconds to detect fraud during live payments and transfers.
- Unified case management: Centralised investigation tools provide clear evidence, case details, and automated audit trails to help teams resolve alerts faster.
- Regulatory compliance and reporting: The platform must support local reporting requirements and offer flexible cloud or on-premises deployment.
Vyntra
Since we’re writing this guide, we’ll start with our own platform. Vyntra brings payment fraud prevention, internal fraud prevention, AML transaction monitoring, and sanctions screening together in one intelligence layer.
Designed for banks operating across multiple entities and geographies, the platform supports centralised fraud monitoring while keeping data separated to meet local regulatory requirements.
Our key capabilities include:
- Multi-tenant fraud management: Supports multiple banking entities, subsidiaries, and geographies on one platform, with centralised management and separate data access for each entity.
- Three-level data separation: Uses entity, group, and system tags to keep transaction data separate, with access restrictions applied across searches, reports, dashboards, and APIs.
- Centralised oversight with local controls: Allows group-level compliance teams to monitor activity across entities while local teams access only their assigned data and manage requirements specific to their jurisdiction.
- Data residency and security: Supports geographically isolated deployments, role-based permissions, and enterprise authentication through LDAP, SAML, and Active Directory.
- Unified fraud detection and investigations: Combines payment fraud, internal fraud, AML, and sanctions screening through a shared detection engine, case manager, and audit trail.
- Real-time and batch risk scoring: Uses pre-built machine learning models, behavioural profiling, and configurable rules to detect suspicious activity across payment flows.
- Community Scoring & Intelligence (CSI): Adds external network and community risk signals to detect suspicious patterns that individual banks might miss, where data sharing is permitted.
- Flexible deployment and scalability: Supports cloud or on-premises deployment, integrates with existing banking systems, and can use separate Elasticsearch clusters for high-volume tenants.
We understand that Vyntra might not be the right fit for every company, so here are some alternatives to compare.
Read more: Real-time payment fraud prevention solutions for banks
DataVisor
DataVisor provides a financial crime platform designed around a parent-child multi-tenant architecture. The platform allows sponsor banks, payment processors, and multi-entity banking groups to manage risk strategies centrally while giving local subsidiaries control over their operations.
Its key capabilities include:
- Parent-child tenant structure: Central teams maintain global risk policies, while child tenants apply local rule overrides, custom branding, and restricted permissions.
- Combined detection engine: Integrates transaction monitoring, device intelligence, and machine learning models for real-time risk decisions.
- Graph analytics for network fraud: Connects accounts, devices, and counterparties across entities to uncover coordinated fraud networks.
- Automated investigation workflows: Provides centralised case management with automated alert reviews and regulatory report preparation.
NICE Actimize
NICE Actimize delivers enterprise fraud management designed for large financial institutions operating across multiple jurisdictions and product lines. The platform brings together risk signals across digital banking channels, card schemes, and payment rails into a single workspace.
Its key capabilities include:
- Enterprise-wide risk coverage: Monitors transaction flows across commercial banking, retail operations, and payment processing units.
- Unified entity risk intelligence: Combines customer profiling, identity resolution, and external data sources to build ongoing trust scores.
- Comprehensive employee monitoring: Detects suspicious employee activity and breaches of internal controls across core banking applications.
- Cloud-based enterprise infrastructure: Provides data integration, automated strategy testing, and investigation workspaces through its X-Sight cloud platform.
Featurespace
Featurespace (now part of Visa) uses Adaptive Behavioural Analytics to detect unusual activity across card payments, instant transfers, and digital channels. Its multi-tenant platform allows financial institutions to run multiple independent entities within a single software installation.
Its key capabilities include:
- Single-instance multi-tenancy: Supports multiple operating companies within one platform, with separate data access for each tenant.
- Analytics configuration groups: Allows central teams to deploy global risk models while individual tenants create and manage local rules.
- Real-time behavioural profiling: Builds profiles of normal customer behaviour to detect new fraud tactics as they occur.
- Flexible access levels: Provides customised user interfaces and features based on tenant requirements and analyst roles.
Implementation when it comes to large-scale fraud management
For banks operating across multiple entities and countries, the right platform brings fraud detection and investigations together while keeping data separate to meet local regulatory requirements.
Solutions like Vyntra combine payment fraud, internal fraud, AML, and sanctions screening through one intelligence layer. This gives banks centralised oversight, shared investigation tools, and the flexibility to manage fraud risks across different entities and geographies.
FAQs
How does a multi-tenant platform maintain strict data segregation between banking entities?
Multi-tenant platforms keep data separate using methods such as multi-level data tagging, tenant-specific encryption keys, and user access controls. This is designed to prevent staff in one subsidiary from accessing another entity’s customer data, supporting banks’ efforts to meet local privacy requirements such as GDPR.
Can local subsidiaries manage their own fraud rules within a central multi-tenant system?
Modern multi-tenant platforms allow central teams to set group-wide risk policies and compliance rules, while individual subsidiaries can create, test, and update their own detection rules based on local fraud risks and customer behaviour.
Why is real-time cross-tenant link analysis important for preventing cross-border fraud?
Organised fraud networks often open accounts across different subsidiaries or regions of the same banking group to move illegal funds. Cross-tenant analysis connects permitted risk signals, such as device information, recipient details, and transaction frequency, across entities. This helps central fraud teams identify linked money mule accounts and stop suspicious activity in real time
