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
Model Performance
The performance of ML models and the scoring engine downgrade over time. This is because fraud patterns continuously change. Rule based tool may not detect these new phenomena…
ADELean4Fraud is able to detect these new patterns as the ML models and the scoring engine are automatically updated.
Contact Us Today and Start Your Project!