This thesis investigates anomaly detection, specifically in the context of online advertising. The main obstacle was the lack of labelled data, which required an inventive solution to properly evaluate our framework. As a result, feedback from account managers and analysts was crucial in perfecting our anomaly detection system. The framework presented here covers the company’s two main businesses: Affil- iate Marketing and Media Advertising. The former, which is aimed at advertisers and can monitor around 1,000 advertisers and a million orders a day, and the latter, which is aimed at media advertising and monitors KPIs for publishers, DSPs and AD spots in more than 100 countries.