Long ago, the introduction of the internet moved crime from physical to digital locations, where anti-fraud actors play a high-stakes game of detection and prevention, always working to stay one step ahead of fraudsters. The battles of modern-day cybercrime follow the same pattern, with one major difference – cybercriminals are far more sophisticated than they... Continue Reading →
Fraud Detection by Stacking Cost-Sensitive Decision Trees
Recently, we published a research paper showing how it is possible to detect fraudulent credit card transactions with a high level of accuracy and a low number of false positives. By using ensembles of cost-sensitive decision trees, we can save up to 73 percent of losses stemming from fraud. Here’s how. Classification, in the context... Continue Reading →
From Real-Time Learning to Reinforcement Learning with Asynchronous Feedback
Online, or real-time, transactional fraud detection systems have recently created quite the buzz in the info security industry. They are an appealing concept: Because we know that fraud patterns change over time, the ability to use machine-learning algorithms to automatically learn new patterns instantly allows us to have a stronger defense system. We often find... Continue Reading →