Motivation for Building an open source database based on EBA’s Standardized NPL Templates
In a recent insightful piece “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the NPL problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.
First Release: OpenCPM NPL Database:
Further building out the OpenCPM set of tools, we took the European Banking Authority’s recommended Non-Performing Loan templates and created an #opensource production grade database for capturing NPL portfolio data sets. Besides providing a powerful storage medium significantly superior to using spreadsheets, the database should facilitate efficient further analysis of such portfolios by building analytics on top of a standardized API.
The backend is built on postgres, one of the most trusted, advanced and widely used open source databases. Setting up an instance using our Docker image should be possible in less than a minute - and did we mention its free and open source? (Released under the Apache 2.0 license).
How to try out the NPL database
It roughly consists of issuing the following commands:
First grab the image from the Docker Hub:
`sudo docker run openrisk/opencpm_npl_backend:0.1`
Once the pull is complete, a container with the initialized database will become available. Check the container ID and issue:
`sudo docker exec -it <container_id> bash`
You are now inside the container environment and ready to interact with the database (named npldb, as user npldb_user)
`psql npldb -U npldb_user`
If all went well, issuing \dt at the postgres shell should show you the standardized tables and relations of the NPL templates and with, e.g. \d+ loan you will get the rundown on data fields per loan contract:
You are now ready to insert some NPL data and get started with #machinelearning, #artificialintelligence or anything else you would like to do to become part of the solution to the NPL problem!
Please let us know if it worked or any other feedback!