Note: this event will be Livestreamed.
Today, credit and debit card fraud detection machine learning models are a critical component of a financial institution’s fraud mitigation operations. Predictive performance of these models is extremely important to help catch fraudsters and shut down a customer’s compromised card as soon as possible. Because of this, data scientists often focus all efforts on the training phase of the model life cycle, trying to squeeze out as much predictive power as possible. In highly regulated U.S. banks, and really anywhere one is deploying machine learning models for critical business results, carefully delivering the models that final mile into production can be just as important. In this talk, we explore
two ways data scientists can help deliver in the final mile: Gradient Boosting Machine (GBM) fraud model interpretability and model monitoring.
Matthew Schlachtman has worked at Wells Fargo for five years and has eight years of data science experience overall. Matthew’s role at Wells Fargo has had many different terms, including data scientist,
data engineer, and machine learning engineer, as well as the less defined UAT engineer or data wrangler. Lately, his background in systems architecture, and experience in Machine Learning and H2O have led him to be a lead project architect in the design of a MaaS Architecture at Wells Fargo. Matthew studied autonomous robotics in grad school and eventually transitioned to using his Computer Science and Artificial Intelligence background for non-physical applications.
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