Data and Machine Learning are the Keys to Surviving the Retail Apocalypse

If you work in retail, you’re likely well-aware of the ongoing “retail apocalypse,” a term that became mainstream in 2017 after the closing and planned closings of thousands of major retail stores and shopping malls across the U.S. According to a recent Business Insider report, approximately 9,000 retail stores closed in 2017, and Cushman & Wakefield estimates that 12,000 retail stores will close in 2018. The reasons retailers are closing stores at such an astounding rate are many – consumer shopping habits have changed, retail juggernauts like Amazon and Walmart are devouring the market, and fraudsters have become sophisticated and extremely tech-savvy.
So how can retailers avoid becoming victims of the retail apocalypse?
The Value of Data
Modern businesses understand the value of data, and for many companies in nearly every industry, data drives almost every part of their business – from marketing and sales, to operations and security. With the help of machine learning (ML), companies are collecting, analyzing, and leveraging data to provide personalized shopping experiences for customers, improve logistics and supply chain management, prevent new account and credit card fraud, and so much more.
Fraud is a Growing Problem for Retailers
Today’s retailers must figure out how to remain competitive in a global marketplace while also trying to keep up with ever-changing fraud trends. Fraud is a growing problem for retailers, especially retailers with online stores. According to a Javelin Strategy & Research study, card-not-present (CNP) fraud in the U.S. increased 40% in 2016. In addition, the rising adoption of EMV cards and terminals are driving many fraudsters to open new fraudulent accounts. It is crucial for businesses to effectively fight fraud and keep their fraud rate below 1%. If the fraud rate goes above 1%, the company not only risks losing the ability to accept credit cards but also loses revenue from an increasing number of chargebacks.
Data is Critical When It Comes to Fighting Fraud
Data is critical when it comes to preventing new account, card not present (CNP), and other types of online fraud. Even the largest online retailers with teams of data scientists and cutting-edge ML algorithms will fail at fighting fraud if they don’t feed their fraud models the right quality data. Helping retailers improve their machine learning fraud models is one of the reasons we developed Identity Check.
Ekata Identity Check gives businesses access to real-time global data and network insights across the five core consumer data attributes of email, phone, person, address, and IP. Identity Check includes Confidence Score, a machine learning-powered solution that provides a comprehensive assessment of each transaction by leveraging the millions of patterns across our Identity Network and Identity Check’s 70+ data signals.
We offer several ways to integrate Identity Check data into ML fraud prevention models and systems through our Identity Check API. The API returns identity information such as validity flags, positive and risk attributes, velocity checks, and match statuses between key consumer data elements. When integrated with ML fraud detection models and applications, the API helps companies catch discrepancies, fight chargebacks, and quickly weed out fraud.
With the data provided by Identity Check, retailers can use one source to view identities from many different angles, allowing businesses to be confident in their assessment of new and returning customers.
Learn More About Ekata Identity Check
For more information about Identity Check, visit the product web page. If you’re interested in seeing a live Identity Check demo, submit a request online.

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