Loan Credit Risk with SQL Server

> **Note:** You can read more about this solution and deployment guides in the [Loan Credit Risk solution](https://github.com/Microsoft/r-server-loan-credit-risk) published on GitHub. > **Required preliminary agreement:** You need to accept the Terms of Use for the Data Science Virtual Machine on your Azure Subscription before you deploy this VM the first time. Click [here](https://portal.azure.com/#blade/Microsoft_Azure_Marketplace/LegalTermsSkuProgrammaticAccessBlade/legalTermsSkuProgrammaticAccessData/%7B%22product%22%3A%7B%22publisherId%22%3A%22microsoft-ads%22%2C%22offerId%22%3A%22standard-data-science-vm%22%2C%22planId%22%3A%22standard-data-science-vm%22%7D%7D) to agree to these terms. ## Overview If we had a crystal ball, we would only loan money to someone we knew would pay us back. A lending institution can make use of predictive analytics to reduce number of loans they offer to those borrowers most likely to default, increasing the profitablity of their loan portfolio. This solution uses simulated data for a small personal loan financial institution, building a model to help detect whether the borrower will default on a loan. Read more about this solution, including step-by-step instructions on how to deploy it, at the [Loan Credit Risk Website](https://microsoft.github.io/r-server-loan-credit-risk/). ## Pricing Your Azure subscription used for the deployment will incur consumption charges on the services used in this solution, approximately $4.12(USD)/hour for the default VM. >Please ensure that you stop your VM instance when not actively using the solution. Running the VM will incur higher costs. > >**Please delete the solution if you are not using it.** ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets.