As part of my Data Science class with General Assembly, we each gave a presentation about a real-world application of data science. My talk was about using machine learning to detect fraud on Skype, and was based upon an excellent paper by Microsoft Research published in November 2013. Although Skype already had measures in place to detect fraud (e.g., credit card fraud, spam instant messages), the research team's goal was to improve the detection of "stealthy fraudulent users" that evade Skype's defenses for a prolonged period. They built a machine learning classifier that flagged potentially fraudulent users, and was able to detect 68% of these users with a false positive rate of 5%. The novelty in their approach was the fusing of disparate data types (profile information, Skype product usage, and Skype social activity) into a single classifier.