When considering the ways in which online businesses protect themselves in their dealings online, how do you think security is handled? As online commerce fraud can cost companies more than $12 billion per year, with more and more businesses beginning to strictly operate online, the problem will only worsen. While tackling fraud tactics in-store is fairly straightforward with digital security cameras and on-site observation, detecting duplicitous action online is more problematic: How do eCommerce sites separate legitimate transactions from fraudulent purchases using only digital activity data? In these instances, Python is helping change the game as part of the Amazon Fraud Detector service, which leverages massive datasets and advanced machine learning to create robust and responsive detection models capable of taking into account multiple user behaviors and identifying potential fraud within minutes. As this service works toward busting those fraudulent individuals, businesses can spend more of their time innovating and creating additional value for their customers. Interested in learning more about the ways in which Python-powered security tools are improving the ways businesses are defending themselves online? Please read on to the infographic included alongside this post.
Python Programming & Its Importance In Machine Learning provided by Accelebrate, a company offering courses in Python Data Science training
