Risk analytics illustration

Risk Analytics Features

Improve fraud prevention across multiple digital channels with a self-learning solution that uses machine learning and data modelling

Risk analytics illustration

Risk Analytics Features

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Fraud Detection & Prevention

Account Takeover Mitigation

Pre-built, configurable rules and policies help prevent fraudulent or unauthorized changes such as: 

  •  New payee creation
  •  User profile account changes via phishing, social engineering, etc. 
  •  Fund transfers  
     

Device Trust Monitoring

  • Proactive identification of new devices for known and unknown users 
  • Automatic device correlation and mapping of device location and usage patterns to determine trust
  • Session-aware, transaction risk analysis of device, application, and transaction data between the mobile device and the financial institution 

Transaction Fraud Monitoring

  • Mobile risk device monitoring identifies fraudulent transactions based on transaction data and beneficiary payments history 
  • Custom rule definition allows banks to define transactional rules and policies to help identify known and emerging fraud
     
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PSD2 Compliance

Mobile Risk Device Monitoring

Mobile risk device monitoring identifies fraudulent transactions based on transaction data and beneficiary payments history.

Malware Activity Monitoring

Malware activity monitoring analyzes potential signals of malware in sensitive and non-sensitive operations, such as transactions, logins, registrations, changes of address, etc.

Continuous Risk Monitoring

Multi-layered monitoring analyzes device, application, behavioral, and historical data, as well as digital channels and server-side analytics.

Pre-built Rules and Policies

Pre-built, configurable rules and policies help ensure and streamline PSD2 compliance.

Customizable Reporting

Customizable reporting allows organizations to demonstrate PSD2 compliance to auditors.

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Visual Analytics

Digital Channel Unification

Digital channel unification automatically aggregates and monitors all transactions, potential fraud attempts, and suspicious activities.

Web-based Case Management

OneSpan Risk Analytics provides centralized case management for high-risk transactions. From a single UI, analysts can open and review fraud cases, and analyze key fraud indicators in context, such as: 

  • IP addresses
  • Relationships
  • Devices 
  • Locations
     

Fraud Visualization Tools

Interactive fraud visualization tools quickly identify the source, destination, device, and location of potential fraudulent transactions.

deployment options

Deployment

Public Cloud

The OneSpan Risk Analytics instance can be hosted on a public cloud, leveraging the latest functionality and security updates with high availability, daily back-ups, disaster recovery (as an option), 24/7 monitoring, and intrusion protection. GDPR compliance ensures each tenant is fully encrypted and provides a streamlined process to remove PII on demand.

On-premises

The OneSpan Risk Analytics instance can be hosted on your own servers behind your firewall. Leverage your existing resources and security controls to comply with your organization’s security policies. Provide the highest network performance with your existing infrastructure.

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Intelligent Automation

Machine Learning-based Risk Analysis

Machine learning-based risk analysis reduces the number of fraudulent cases that need to be reviewed by fraud analysts and results in fewer resources needed to monitor for fraud.

Automated Fraud Alerts

Automated fraud alerts proactively identify and highlight potential fraudulent events for a fraud analyst to review in a timely manner.

Web-based Case Management

Centralized case management of all high-risk transactions – all in a single UI.

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Seamless Security

Machine Learning and Data Modeling

Powered by real-time machine learning and data modeling, risk-based analysis helps to mitigate fraudulent transactions more quickly with fewer false positives.

Automated Risk Profiling

Automated risk profiling analyzes user, application, and transaction data to identify transactions that are out of the usual scope and contextual pattern.