If we had a crystal ball, we would only lend money to someone who we knew would pay us back. A lending institution can make use of predictive analytics to reduce the number of loans they offer to those borrowers most likely to default, thereby increasing the profitability of their loan portfolio. This solution uses simulated data for a small personal loan financial institution, building a model to help predict whether a borrower will default on a loan.
The business user uses the predicted scores to help determine whether or not to grant a loan. They fine-tune their prediction by using the PowerBI Dashboard to see the number of loans and the total cost saved under different scenarios. The dashboard includes a filter based on percentiles of the predicted scores. When all the values are selected, they view all the loans in the testing sample, and can inspect information about how many of them defaulted. Then, by checking just the top percentile (100), they drill down to information about loans with a predicted score in the top 1%. Checking multiple continuous boxes allows them to find a cut-off point they are comfortable with to use as a future loan acceptance criterion.
Use the “Try It Now” button below to view the PowerBI Dashboard.
Data Scientist Perspective
SQL Server R Services brings the compute to the data by running R on the computer that hosts the database. It includes a database service that runs outside the SQL Server process and communicates securely with the R runtime.
This solution walks through the steps to create and refine data, train R models and perform scoring on the SQL Server machine. The final scored database table in SQL Server gives a predicted score for each potential borrower. This data is then visualised in PowerBI.
Data scientists who are testing and developing solutions can work from the convenience of their R IDE on their client machine, while pushing the compute to the SQL Server machine. 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.
Use the “Deploy” button below to create a Virtual Machine that includes the data, R code, SQL code and an SQL Server 2016 database (Loans) containing the full solution.
Your Azure subscription used for the deployment will incur consumption charges on the services used in this solution, approximately $1.15/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.
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