Bank Nonperforming Loans | World Bank Open Data

The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy. The ratio of bank nonperforming loans to total gross loans measures bank health and efficiency by identifying problems with asset quality in the loan portfolio. A high ratio may signal deterioration of the credit portfolio. This release contains bank nonperforming loans to total gross loans (%) for all countries in the world.

The original publisher of this data is The World Bank. This content is published as World Bank Open Data and provides free and open access to global development data. This work is licensed under a Creative Commons Attribution 4.0 (CC-BY 4.0). This data is anonymized/aggregated.

• Source - International Monetary Fund, Global Financial Stability Report 
• Schema Definitions 
• Sample Dataset 
• Terms of Use 
• World Bank Open Data Homepage 
• Frequency: Annual

You will receive access to the following:

• Bank nonperforming loans to total gross loans (%) (bank-nonperforming-loans.csv)
• CloudFormation template that setups up automatic revision updates plus AWS analytics services such as AWS Glue and Amazon Athena (cloudformation.yaml)
• AWS Lambda code for revision updates (post-processing-code.zip)

Please note, in the post processing code, we use a Lambda layer that extends the AWS Python SDK (boto3) that is built into the Lambda Python runtime by adding the AWS Data Exchange and AWS Marketplace Catalog API SDKs as of November 13, 2019. Once the public SDKs are updated to include AWS Data Exchange APIs, we will update the code to remove this Lambda layer.

Assuming you have subscribed to this product listing, below are the detailed steps to deploy CloudFormation template:

(Please note that you will need IAM permissions for CloudFormation, AWS Data Exchange, IAM, Lambda, Glue, Athena and QuickSight, in order to deploy the CloudFormation template.)

• Under the product listing, scroll down to Data sets section and click on the Data set name
• Under the Revisions section, click on the most recent revision
• Under Assets, checkmark bank-nonperforming-loans/automation/post-processing-code.zip and click Export to S3
• Choose the S3 Bucket where you would like to store the dataset. Make sure you only choose the S3 bucket. The asset comes with a pre-defined directory structure
• Under Assets, checkmark bank-nonperforming-loans/automation/cloudformation.yaml and click either Export to S3 or Export to computer
• If you exported the cloudformation.yaml to S3, go to the S3 UI on the AWS console and navigate to the location where the cloudformation.yaml is stored. In S3, click on the cloudformation.yaml and copy the url from the Object URL
• Now, from your AWS Management Console, log onto Amazon CloudFormation UI and click Create Stack
• Under Choose a template either provide the template via uploading from local computer or specify the S3 object url and click Next
• Provide a friendly stack name in the Stack name text box
• In the SourceS3Bucket field, input the S3 bucket name that you chose earlier to store the bank-nonperforming-loans/automation/post-processing-code.zip file
• Leave rest of the fields as is
• Click Next
• In the Options screen, click Next
• Tick mark the I acknowledge that AWS CloudFormation might create IAM resources. box
• Click Create

• Lambda function to setup automatic AWS Data Exchange revision updates for this dataset
• CloudWatch Event rule that will automatically trigger the Lambda function every time a new revision update is published
• Another Lambda function to setup AWS Glue and Amazon Athena
• Necessary IAM roles and permissions

If you are interested in looking at the AWS Lambda code or the CloudFormation template, feel free to inspect files inside bank-nonperforming-loans/automation/post-processing-code.zip and bank-nonperforming-loans/automation/cloudformation.yaml

Apart from the source data, what we are also providing in this product listing is an easy way to interact and extract value out of the dataset. Native AWS Analytics services such as AWS Glue, Amazon Athena and Amazon QuickSight provide different ways to interact and visualize the data. The included AWS CloudFormation template sets up AWS Glue and Amazon Athena automatically in your AWS account.

Data Analysis - This diagram shows how all the AWS services interact 

Once the CloudFormation template is successfully deployed, the data is immediately searchable, queryable, and available on Athena. You can go to the Athena UI from the AWS Management Console and run SQL queries on the dataset.

# list bank nonperforming loans for all countries for year 2017

# list yearly bank nonperforming loans for "china"

# compare bank nonperforming loans for "china between year "2010" and "2017"

# compare bank nonperforming loans between "brazil" and "china"

Below are the detailed steps to analyze dataset using Amazon QuickSight

• From your AWS Management Console, log onto Amazon QuickSight
• Click Manage data
• Click New data set
• If you ran the provided CloudFormation template, you should already have your database and table with schema created in AWS Glue and Athena
• Click on Athena to connect to your data source
• Provide a name for your QuickSight Data source name and click Create data source
• In the Database: contain sets of table dropdown, choose database as bank_nonperforming_loans and under Tables: contain the data you can visualize, choose table as data
• At this point, you can Edit/Preview data if you like
• You can then click on Select
• In the Finish data set creation screen, you can select Visualize to finish the creation of data set process
• Visualize the data set by selecting the Horizontal bar chart from the Visual types
• Drag country_name field to the Y axis in Field wells and for e.g. drag 2017 field in the Value block to chart the data

You are now ready to start analyzing and visualizing the dataset.

If you have questions about the source data, please contact data@worldbank.org . If you have any questions about the CloudFormation stack, Lambda code or any of the AWS services being used, please contact data@rearc.io .

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