Feature Engineering – The Magic Behind Great Machine Learning Outcomes

In this second webinar in our six-part series, Socure’s VP Data Science, Pablo Abreu, discusses the methods used in feature engineering to go from raw data (discussed here Data Exploration - Improving the Foundation) to highly predictive features. More specifically, Pablo will share the approach Socure takes to build proprietary predictors to boost machine learning model performance.

In this webinar, Pablo will share:

  • General concepts and approaches to feature engineering
  • Typical challenges encountered with feature engineering
  • Several best practice approaches to consider to maximize success
  • Considerations for manual vs. automated approaches to feature engineering

On-Demand Webinar

  • Rivka Gewirtz Little

    Rivka Gewirtz Little is a financial crime and payments solution expert with nearly two decades of FinTech market experience. Before rejoining Socure as chief of staff to the CEO, she led global fraud strategy in the treasury of Goldman Sachs. Prior, she served as the SVP of marketing for Socure during the largest growth phase in company history. Earlier, Little was an industry analyst responsible for the Global Payment Strategies practice at IDC Financial Insights. There, she focused on the intersection of payments modernization, fraud, and identity. Her technology coverage has been cited in publications, including The New York Times, USA Today, CNET, Vox, The Verge, and a number of technology trade journals.
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