In this talk, we will discuss the process of building a sophisticated fraud model at a large, highly regulated financial services company. It will also touch on the approach we used in building a real-time model in a big data environment, deploying that model to production, and the work involved in maintaining and monitoring model performance over time.
Daniel Dixon is a senior data engineer in the Enterprise Analytics & Data Science team at Wells Fargo, where he responsible for designing and building scalable, big data pipelines to feed intelligent systems across the bank. In that role he specializes in big data and advanced analytic challenges, utilizing machine learning, statistics, process optimization, and visualization techniques to analyze and assemble large, complex datasets. Daniel joined Wells Fargo in 2014. Previously, he spent five years as a professional services consultant for Teradata, with a focus on visualization and ETL technologies. He holds degrees in Electrical Engineering and Computer Science from Georgia Tech.