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4 Solutions to Flag Suspicious E-Commerce Orders in Real-Time

Online retailers are likely seeing an increase in overall transactions ahead of the holidays, but they need to remain vigilant in monitoring for activity that may signify fraud.

A spike in the sales of a particular product or a transaction with an extremely high dollar amount, for example, should be reason for concern and immediate action. If e-commerce retailers are able to automatically reject orders and narrow down the pool for potential fraud, the results can be quite positive.

But how do you know if an order is good or bad? Many e-commerce companies and brands are now exploring machine learning and other advanced technologies to help mitigate fraud and increase sales. Using these solutions not only helps improve the customer experience, it will result in improvements in other areas critical to the enterprise including the order decline rate (an indicator of how well sellers are doing when it comes accepting more good orders in general). 

Companies like Sift Science, Forter, Signifyd, Riskified and others currently offer powerful machine-learning solutions that are able to flag suspicious orders automatically in real-time, so that retailers do not have to do this this work manually (which is often fraught with errors).

  • Sift Science

    Sift Science

    Uses machine learning and other automation technologies to protect businesses from a variety of fraud and abuse.

  • forter


    An end-to-end identity-based fraud protection solution that providers sellers with a robust framework for eliminating fraud and reducing abusive behavior.

  • Signifyd


    A popular fraud prevention and chargeback solution that's addressing those e-commerce issues through technology, data, and design.

  • Riskified


    A unique fraud prevention and chargeback solution that leverages self-optimizing machine learning models to distinguish between good and bad orders.

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