Loan Chargeoff Prediction Solution Templates are Live in Cortana Intelligence Solutions Gallery


A charged off loan is a loan that is declared by a creditor (usually a lending institution) that an amount of debt is unlikely to be collected, usually when the loan repayment is severely delinquent by the debtor. Chargeoff loan has huge financial implication on lending institutions’ financial statement.  No banks or credit unions like to have high percentage of chargeoff loans in their books. Having data ahead of time of which loans are most likely to be charged off will buy tremendous lead time for the banks to save the loans from charging off. Loan manager could formulate personalized plan with the borrower on how to continue with loan repayment rather than incurring the loan as a chargeoff.

For more information, read this blog: End to End Loan ChargeOff Prediction Built Using Azure HDInsight Spark Clusters and SQL Server 2016 R Service

We have published two solution templates deployable using two technology stacks for the above chargeoff scenario:-

  1. Loan Chargeoff Prediction using SQL Server 2016 R Services – Using DSVM with SQL Server 2016 and Microsoft ML, this solution template walks through how to create and clean up a set of simulated data, use 5 different models to train, select the best performant model, perform scoring using the model and save the prediction results back to SQL Server. A PowerBI report connects to the prediction table and show interactive reports with the user on the chargeoff prediction.
  2. Loan Chargeoff Prediction using HDInsight Spark Clusters – This solution demonstrates how to develop machine learning models for predicting loan chargeoff (including data processing, feature engineering, training and evaluating models), deploy the models as a web service (on the edge node) and consume the web service remotely with Microsoft R Server on Azure HDInsight Spark clusters. The final predictions is saved to a Hive table which could be visualized in Power BI.

With just a few clicks to deploy the solution to their Azure subscriptions, external customers can explore the raw data, understand the data transformation, R model engineering and operationalization of the models. PowerBI report also allows the customers to explore the chargeoff historical and prediction data. Internal Microsoft sales and field team could also quick deploy the solution and demo to the customers on the end to end data pipeline and R model development.

We have also published the same solutions to GitHub, please play around with the solution templates and provide your feedback, here are some of the link on the blog and GitHub repo:-

Blog:- https://blogs.msdn.microsoft.com/rserver/2017/06/29/end-to-end-loan-chargeoff-prediction-built-using-azure-hdinsight-spark-clusters-and-sql-server-2016-r-service/

GitHub:- https://microsoft.github.io/r-server-loan-chargeoff/index.html

Solution Template in Cortana Intelligence Gallery:- https://gallery.cortanaintelligence.com/Solution/Loan-ChargeOff-Prediction-with-SQL-Server

https://gallery.cortanaintelligence.com/Solution/Loan-ChargeOff-Prediction-with-Azure-HDInsight-Spark-Clusters

 

Ajay Jagannathan (@ajaymsft)

Principal Program Manager


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