#Online Fraud Detection Template with SQL Server 2016 R Services
In this template, we demonstrate how to develop and deploy end-to-end Fraud Detection solutions with SQL Server 2016 R Services.
In this template, the online purchase transaction fraud detection scenario (for the online merchants, detecting whether a transaction is made by the original owner of payment instrument) is used as an example. We solve the Fraud Detection as a binary classification problem.
The solutions are demonstrated using a Online Transaction data, with the following files:
| File | Description |
|---|---|
| .\Data\Online Fraud-Fraud Transactions.csv | Raw fraud transaction data |
| .\Data\Online Fraud-Untagged Transactions.csv | Raw transaction data without fraud tag |
For a full description of the template, please refer to the template in Cortana Analytics gallery.
In this template with SQL Server R Services, we show two version of implementation:
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Model Development with Microsoft R Server in R IDE. Run the code in R IDE (e.g., RStudio, R Tools for Visual Studio) with data in SQL Server, and execute the computation in SQL Server.
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Model Operationalization In SQL. Deploy the modeling steps to SQL Stored Procedures, which can be run within SQL environment (such as SQL Server Management Studio) or called by applications to make predictions. A powershell script is provided to run the steps end-to-end.
The following is the directory structure for this template:
- Data This contains the provided sample data.
- R This contains the R development code (Microsoft R Server). It runs in R IDE, with computation being done in-database (by setting compute context to SQL Server).
- SQLR This contains the Stored SQL procedures from data processing to model deployment. It runs in SQL environment. A Powershell script is provided to invoke the modeling steps end-to-end. See Readme files in each directory for detailed instructions.