Alaric announces release of fractals version 3.7

Latest version of the world’s first and only PA-DSS certified fraud detection and prevention software includes key enhancements.

Alaric, a leading international provider of payments and enterprise fraud detection systems, has announced the next generation of its unique enterprise fraud management solution, Fractals 3.7. The release of Fractals version 3.7 includes a host of new features designed to improve enterprise-wide fraud detection, increase governance and compliance, and streamline fraud and risk analyst activities.


Fractals 3.7 is still the world’s first and only PA-DSS certified fraud detection and prevention software, an accreditation that instantly sets the system apart from competitive solutions which have yet to achieve the seal of the Payment Application Data Security Standard.

Fractals enables a unique multi-institution, multi-lingual approach to enterprise fraud detection, allowing financial institutions, processors and retailers across the globe to deploy a holistic fraud management solution at an enterprise level. It is a single, integrated solution for issuer and acquirer fraud detection, providing economies of scale in hardware, database licences, staffing and training.

Key enhancements to Fractals 3.7 include:


  • Unique self learning detection models which improve detection rates by up to 20%
  • Holistic rules enablement
  • User-specified variables which are defined dynamically


Peter Parke, Fraud Solutions Director at Alaric, says “Fractals remains the world’s first and only PA-DSS certified fraud detection and prevention software. We are excited to bring to market a number of enhancements to a solution which is already used by more than 145 organisations around the world. Fractals 3.7 is now the recognised benchmark product for the global payment fraud prevention industries, however, as always we will remain focussed on continually enhancing functionality, improving product performance and gaining market share.”

Fractals uses self learning models together with in flight, real time transaction blocking to drive down fraud losses. The result is vastly increased detection rates, reduced false positives and an increased percentage of fraud cases detected on the first fraudulent transaction.

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