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Fraud Analytics

Fraud Analytics

An Anomaly Detection Engine to support (unkown) fraud patterns identification

What anti-fraud organizations are currently facing

  • Fraud patterns change continuouslyand more rapidly than ever
  • Rule-based systems are unable to detect unknown fraud patterns
  • Increase the number of real fraud patterns identified
  • Keep the number of «potential frauds» below a sustainable threshold to not overload the analystteams
  • Improve the effectiveness of the analyst teams by focusing their activities only on suspicious patterns

Our Solution

ADELean4CSA is technical framework that has a non-supervised Machine Learning engine at core, who continuouslylearns,and without any pre-determined rules, is able to detect even unknown suspicious fraud patterns.

By implementing the framework, analysts may improve their efficacy and organizations may prevent frauds more effectively.

Application Examples:
  • Pre-paid / Rechargeable Cards
  • Debit Cards
  • Credit Cards

Why ADELean4Fraud is unique

  • 100% non-supervised Machine Learning
  • Models are built directly on customers’ data
  • Totally open (it can be integrated easily with customer existing frameworks and tools)
  • 0 Data Loss by default
  • Designed with high availability architecture (full data recovery)
  • Highly reliable architecture
  • Predictive models may be used to alert when a «not-yet-performed» transaction is a fraud… so that it can be blocked