Fraud is a top concern for retailers, especially when it involves costly devices like smartphones or televisions. For every one device “purchased” with a fraudulent credit card, retailers must sell five more in order to recoup those losses. Consensus Corporation has built a fraud detection model as part of its Revenue Cloud on the Connected Commerce platform in order to alert retailers of potential activity before it occurs. The more efficiently Consensus can update this model with new retail and consumer data, the more money it can save retailers—even just a small percentage of efficiency gains could translate to millions saved.
Matt Derda and Harrison Lynch explain how Consensus leverages the combined power of data wrangling and machine learning to more efficiently identify and reduce retail fraud and how adopting data wrangling technology has helped Trifacta reduce time spent data wrangling from six weeks to one week.
Matt Derda is a customer success manager at Trifacta. Previously, Matt was a CPFR (collaborative planning, forecasting, and replenishment) analyst at PepsiCo, where he worked with Trifacta to accelerate the preparation of customer supply chain data to more accurately and quickly forecast sales.
Harrison Lynch is the Senior Director of Product Management for the Consensus Marketplace Cloud and Risk Cloud products. Ever the Digital Sheriff, even when deep in the midst of a re-platforming and developing an API product offering, he keeps himself awake at night pondering how to prevent identity thieves from stealing iPhones and how to manage multiple Product Managers and dev teams. Harrison has spent the past fifteen years of his career in the niche world of integrating cell phone carrier systems to web applications, while trying to stop fraudsters. He loves a good nerd fight, and has never seen a Product Management blog that he won’t dive into.
Harrison is a 1995 graduate of the University of Oregon.
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