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...

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...

Applying Data Science to Fraud Prevention

Eighty thousand Kindle users. Sixty-five million Tumblr users. What do they have in common? Both groups had their login credentials breached, courtesy of hackers. While these attacks didn’t directly target financial accounts,the information contained in these breaches is likely being sold on the Dark Web and being used to build a larger profile that will...

Fraud Detection That Accounts for Misclassification Using Cost-Sensitive Logistic Regression

Fraud detection is a cost-sensitive problem, in the sense that falsely flagging a transaction as fraudulent carriesa significantly different financial cost than missing an actual fraudulent transaction. In order to take these costs into account, companies should use a more business-oriented measure such as “Cost,” which allows companies to make decisions that are better aligned...

Evaluating a Fraud Detection Using Cost-Sensitive Predictive Analytics

A credit card fraud detection algorithm consists in identifying those transactions with a high probability of being fraudulent based on historical fraud patterns. The use of predictive modeling/machine learning in fraud detection has been a topic of interest in recent years. Different detection systems based on machine learning techniques have been successfully used for this problem,...

Feature Engineering for Fraud Detection Models

As cybercriminals are constantly updating their strategies to avoid being detected, traditional fraud detection tools, such as expert rules, are less effective as they do not incorporate recent fraud patterns as fast as the fraudsters are changing their behavior. To incorporate the fraudulent behavior fast, it is important to use advanced machine learning algorithms, such as...

Fraud Detection with Advanced Outlier Detection Algorithms

Fraud Detection with Advanced Outlier Detection Algorithms Online fraud costs the global economy more than $400 billion, with more than 800 million personal records stolen in 2013 alone. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative fraud patterns emerge. One technique organizations use to detect and prevent...