This collection of experiments demonstrates the steps of the Online Fraud Detection Template on how to build and deploy an online transaction fraud detection model.
Fraud detection is one of the earliest industrial applications of data mining and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts in the **Execute R Script** module, to enable an end-to-end solution. Fraud detection is typically handled as a binary classification problem, but the class population is unbalanced because instances of fraud are usually very rare compared to the overall volume of transactions. Moreover, when fraudulent transactions are discovered, the business typically takes measures to block the accounts from transacting to prevent further losses. Therefore, model performance is measured by using account-level metrics, which will be discussed in detail later. This template uses the example of online purchase transactions to demonstrate the fraud detection modeling process. This process is put into 5 separate experiments, with each containing one of the following steps. ![workflow] [workflow]:https://az712634.vo.msecnd.net/samplesimg/v1/T3/workflow.PNG