Fraud.Net

Fraud.Net

Fraud Analytics

Founded: 2013
HQ: New York City / United States

Fraud.net is the leader in crowdsourced e-commerce fraud prevention, currently protecting over 2% of all US e-commerce. Its unified algorithmic architecture combines 1) collective intelligence, 2) cognitive computing (such as dynamic machine learning) and 3) rules-based decision engines to detect fraud in real-time, at scale. The platform delivers unprecedented business intelligence and embedded control through its comprehensive risk management and analytics solutions.

Process Steps

  • Risk Engine
  • Data Provider
  • Manual Review Cockpit
  • Chargeback Handling
  • Callcenter Fraud
  • KYC/AML/ID Verification

Technology Coverage

  • Machine Learning
  • Device Fingerprinting
  • Behavioral Analytics
  • Chargeback Guarantee

Reference Customer

Fingerhut.com

Fareportal.com

JackThreads.com

GreenMarket.com

MotherNature.com

Integration

Direct Integrations Only

Fraud Solution Profile

Fraud.net is the only cloud-based ‘glass-box’ system, offering a full and transparent presentation of the data, statistics, contextual variables, and other factors that power an automated or computer-assisted decision. The customer-centric, end-to-end solution can be implemented as a stand-alone system or used to augment the predictive capabilities of a client’s existing program.  Fraud.net is also the only solution that effectively counters tough-to-detect ‘friendly fraud’ and provides a full suite of live fraud analytics and data-mining capabilities.

Fraud.net’s robust technology offers merchants:

Cutting-edge platform featuring industry-leading data management and analysis, petabyte-level storage, ultra-low-latency processing within a highly secure VPC environment

Leverages event-based computing, distributed processing, and massive parallel processing data warehouse

APIs search billions of records, conduct thousands of fraud checks and generate 99%+ scoring accuracy for each transaction, returning complete results in less than 300ms

 

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