Network Intelligence

Distribute, Exchange and Consume Risk Signals

Improve your fraud prevention strategies with network intelligence. USP’s proprietary network intelligence 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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Leverage Flexible Consortium Data

Choose which fraud signals to apply to your models, with real-time signal sharing across the network

Enrich Your Current Fraud Strategy

Choose which signals to adopt and how to apply them to your fraud strategy

Respond to Real-Time Threats

Gain faster access to macro trends insight to update models in real-time

NETWORK INTELLIGENCE FOR BANKS, PROCESSORS AND ACQUIRERS

Network Collaboration 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 with USP’s best-in-class fraud management

Maintain the strength of custom, proprietary signals and complement with community exchanged signals

Choose your adopted model insights and which contribute to community findings

Leverage transparent, individual signals rather than a single opaque model

Understand localized threats before they become endemic

Incorporate signals from third-party fraud intelligence sources

Create informative and meaningful data features, focused on individual requirements

Powering Award-Winning Network Intelligence Technology

USP Worldwide was awarded Best Wow Insight from Data Award at the Fintech Finance Awards for using data to bring best-in-class fraud management to financial institutions of all sizes.

Leverage Machine Learning Insights 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