Preventing Fraud and Boosting eCommerce with Data Science


The eCommerce industry is growing rapidly year by year. According to eMarketer, in 2020 alone, the year that Covid-19 came into our lives, retail eCommerce sales reached $4.28B. As an industry expanding so fast, it has many challenges, such as high customer expectations, new payment and shipping flows, including omnichannel sales, and more. As with any highly profitable industry, it also attracts many fraudsters, making fraud and payment declines one of the significant causes for lost potential eCommerce revenue.

In this session, we'll discuss those challenges and share how data science, by implementing various supervised and unsupervised models and sophisticated feature engineering based on deep domain expertise, helps us prevent fraud and get the right consumers safely and successfully to the checkout.

Three takeaways:

  • eCommerce is driving a retail boom and customers expect an amazing experience

  • There are a plethora of data science use cases across the eCommerce value chain

  • Data science plays a key role in enabling eCommerce to combat fraud and prevent loss of revenue for merchants

Elad Cohen

VP of Data Science at Riskified

As the VP of Data Science at Riskified, Elad Cohen is responsible for the ongoing improvements of the machine learning algorithms. Elad has over 12 years of experience managing data science and analytics teams across research domains. Passionate about unleashing the power of data to make significant business impact, Elad is an expert in R, machine learning, and statistics. He holds an M.Sc. and B.Sc in Applied Physics from Bar-Ilan University, and an Executive MBA from Tel Aviv University.

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