This post is authored by Bharath Sankaranarayan, Principal Program Manager at Microsoft.
I am pleased to announce the Loan Credit Risk Solution how-to on SQL and HDI. Loan Credit Risk is a very common problem that lending institutions are challenged in assessing which loans are likely at risk.
Loan Credit Risk solution how-to, demonstrates how you can apply machine learning to manage risky loans – the ones that have a high probability of defaulting.
The solution is built using Microsoft R Server 9.1 (April 2017) on both SQL Server 2016 and HDI on Spark (Microsoft R server 9.1 released in June 2017). For detailed information and the features and capabilities you can check out the blog - Microsoft R Server 9.1
The solution how-to was developed for small personal loan financial institution using the borrower's historical financial data coupled with the information while requesting for a loan. The solution how-to was built to cater two personas, the business user in this case a loan officer and the data scientist who is interested in how the data was used to build the models used for the calculating the risk. The goal is to help the lending institution reduce risk on their investment.
To the loan officer, to decide how much risk the institution can take based on the outstanding loans and what are the limits, an interactive dashboard that provides visibility to the test data that was used and a tab that shows the new potential loans with a cutoff value (the risk level that the institution is willing to take). These two provide the loan officer the ability to make the decision on the loan. The output or the predictions (scores) are grouped in percentiles (the higher the percentile, higher the risk).
Now to the data scientist who is interested in how these are developed we have provided detailed steps to follow through. The solution walks through the Data Science Process and shows how to train and score the models in both SQL Server and HDI on Spark Cluster. These are provided in an easy to follow steps for both platforms. The SQL Server implementation leverages the Data Science VM for Windows which provides all the tools you need to try the solution in a single box.
The completed solutions are deployed to SQL Server 2016 by embedding calls to R in stored procedures. These solutions can then be further automated with SQL Server Integration Services and SQL Server agent.
They can also use PowerShell scripts or Jupyter Notebooks, in addition to using IDEs such as R Tools for Visual Studio. Completed solutions are deployed to SQL Server 2016 by embedding calls to R in stored procedures. These solutions can then be further automated with SQL Server Integration Services and the SQL Server agent. We have made the entire code that powers this solution free to use and modify as well.
Similarly, when you deploy the HDI based solution, you will get to experience Jupyter Notebooks in additional to the R IDE. The entire solution will be deployed within your Azure Subscription and you get to experience the HDI on Spark Cluster.
To try this out please visit
Loan Credit Risk Solution how-to on SQL
or Loan Credit Risk on HDI using Spark and send us your feedback to let us know how we are doing. You can also check out our other solutions here.