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. Given that high chargeoff has negative impact on lending institutions' year end financials, lending institutions often monitor loan chargeoff risk very closely to prevent loans from getting charged-off. Using Azure HDInsight R Server, a lending institution can leverage machine learning predictive analytics to predict the likelihood of loans getting charged off and run a report on the analytics result stored in HDFS and hive tables.
> **Note:** You can read more about this solution and deployment guides in the [Loan Chargeoff solution](https://github.com/Microsoft/r-server-loan-chargeoff) published on GitHub. > This solution will create an HDInsight Spark cluster with Microsoft R Server. This cluster will contain 2 head nodes, 2 worker nodes, and 1 edge node with a total of 32 cores. The approximate cost for this HDInsight Spark cluster is 3.11USD/hour. Billing starts once a cluster is created and stops when the cluster is deleted. Billing is pro-rated per minute, so you should always **delete your cluster** when it is no longer in use. Use the Deployments page to delete the entire solution once you are done. ## Overview There are multiple benefits for lending institutions to equip with loan chargeoff prediction data. Charging off a loan is the last resort that the bank will do on a severely delinquent loan, with the prediction data at hand, the loan officer could offer personalized incentives like lower interest rate or longer repayment period to help customers to keep making loan payments and thus prevent the loan of getting charged off. To get to this type of prediction data, often credit unions or banks manually handcraft the data based on customers' past payment history and performed simple statistical regression analysis. This method is highly subject to data compilation error and not statistically sound. This solution template demonstrates a solution end to end to run predictive analytics on loan data and produce scoring on chargeoff probability. A PowerBI report will also walk through the analysis and trend of credit loans and prediction of chargeoff probability. Read more about this solution, including step-by-step instructions on how to deploy it, at the [Loan ChargeOff Prediction Website](https://microsoft.github.io//r-server-loan-chargeoff/). ## 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. ![ ](https://quickstart.azure.ai/track?solutionid=loanchargeoffhdi)