(Machine) learning to detect fraudsters (sponsored by ThoughtWorks)

Join Sarah LeBlanc and Hany Elemary for a unique talk where data science meets DevOps culture. Sarah and Hany explain how to put machine learning fraud detection models into production, using data science algorithms to drive effective models. Along the way, they explain how a global corporation is creating an extensible platform for more than just application fraud.

A client recently tasked Sarah LeBlanc and Hany Elemary with an interesting and challenging problem; detecting credit card application fraud at a global financial institution. The client’s current practice of relying on third-party vendors to manage these models was slow, expensive, and time consuming. Much like hackers, fraudsters are always changing behavior. To keep up with their tricks, the detection algorithms (models) need to be updated continuously. The longer these models go without updating, the less effective they become, losing money and valuable customers to undetected fraudsters. Sarah and Hany’s task was to devise a process to improve their fraud detection with a sophisticated machine learning workflow that empowers data scientists to rapidly and iteratively design and develop new models and put them into production. Sarah and Hany explain how to put machine learning fraud detection models into production, using data science algorithms to drive effective models. Along the way, they explain how a global corporation is creating an extensible platform for more than just application fraud. Sponsored by ThoughtWorks