Reduce Fraud Losses
Improve your fraud prevention strategies with network intelligence. This proprietary technology allows banks, processors, acquirers and networks to securely share and consume industry-wide fraud signals to feed their machine learning models alongside proprietary data.
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Collaborate for Improved Fraud Prevention
Increasing digital payment volumes and types creates increasing fraud volumes and attack vectors. Financial institutions need to collaborate to develop macro insights and beat the fraudsters with valid models that are weighted appropriately across all parties.
- Consume, distribute and exchange secure fraud signals without exposing raw data
- Maintain strength of custom, proprietary signals and complement with signals exchanged within the community
- Incorporate signals from third-party fraud intelligence sources
- Choose which insights to adopt into models and which to contribute to community findings
- Leverage transparent, individual signals rather than a single opaque model dominated by the largest contributor
- Create informative, non-redundant and contextually meaningful data features, focused on individual requirements
- Understand localized threats before they become endemic
Deliver a region-, industry- and market-wide view of emerging threats >
Leverage Machine Learning Effectively
Machine learning insight is only as strong as the data it receives. Systems must be supplied with the widest possible view of risk in real time. This necessitates access to internal and external data.
- Incorporate community data into machine learning model training and risk analysis
- Consume community fraud signals in real time to instantly improve models
- Integrate proven model features into adaptive machine learning strategies
- Leverage a hybrid approach with model creation and risk scoring
- Leverage an intuitive UI for validation, and automatically apply community contributions for accelerated time to value
Network intelligence is the future of machine learning for payments fraud prevention >
