Fraud Detection with R and Azure
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Detecting fraudulent transactions is a key applucation of statistical modeling, especially in an age of online transactions. R of course has many functions and packages suited to this purpose, including binary classification techniques such as logistic regression.
If you'd like to implement a fraud-detection application, the Cortana Analytics gallery features an Online Fraud Detection Template. This is a step-by step guide to building a web-service which will score transactions by likelihood of fraud, created in five steps:
- Generate tagged data
- Data Preprocessing
- Feature engineering
- Train and Evaluation Model
- Publish as web service
Each step makes use of the R language, as seen ion the “Execute R Script” nodes in the screenshot from the “generate tagged data” step below.
The methodology in this template can be easily extended to fraud detection scenarios in other domains. To get started with the template, follow the link below. You can experiment with the template for without registering in Guest Access mode, or sign up for a Free Workspace account to save your changes.
Cortana Analytics Gallery: Online Fraud Detection Template
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