Case Study: Amazon Web Services & Fraud.net

By Whitney Anderson

January 20, 2019

Fraud.net was featured on the Amazon Web Services

Using Amazon Machine Learning, Fraud.net can quickly create and train a number of specific, targeted machine learning models, rather than building a single algorithm to try and capture all the different forms of fraud. We are able to build and retrain models in almost half the time it took on other platforms we tested.

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Fraud.net Unveils "Policy Monitoring" to Strengthen Risk Management and Policy Compliance

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Fraud.net Introduces "Entity Screening" to Transform Compliance and Risk Management

Fraud.net is excited to announce the launch of Entity Screening, designed to streamline compliance and enhance risk management for businesses worldwide. By centralizing and automating the screening, verification, and approvals of businesses and organizations, Entity Screening enables enterprises to reduce costs, save time, and make smarter, faster decisions.

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Visa Acquirer Monitoring Program (VAMP) Compliance with Policy Monitoring

Discover how Policy Monitoring empowers acquirers to master VAMP compliance with proactive strategies, real-time monitoring, and fraud detection. Simplify risk management and meet stricter regulatory standards with ease.

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