https://github.com/open-risk/openlgd
openLGD is a Python powered library for the statistical estimation of Credit Risk Loss Given Default models. It can be used both as standalone library and in a federated learning context where data remain in distinct (separate) servers
https://github.com/open-risk/openlgd
credit-risk-loss credit-scoring decentralized-application federated-learning python-framework
Last synced: 2 months ago
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openLGD is a Python powered library for the statistical estimation of Credit Risk Loss Given Default models. It can be used both as standalone library and in a federated learning context where data remain in distinct (separate) servers
- Host: GitHub
- URL: https://github.com/open-risk/openlgd
- Owner: open-risk
- License: apache-2.0
- Created: 2019-11-17T17:39:21.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-10-14T16:06:28.000Z (about 1 year ago)
- Last Synced: 2025-09-10T06:10:37.164Z (4 months ago)
- Topics: credit-risk-loss, credit-scoring, decentralized-application, federated-learning, python-framework
- Language: Python
- Homepage: https://www.openriskmanual.org/wiki/Loss_Given_Default_Models
- Size: 646 KB
- Stars: 24
- Watchers: 2
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
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README
# openLGD
**openLGD** is a Python powered library for the statistical estimation of **Credit Risk Loss** (Also loss-given-default or LGD) models.
openLGD can be used both as standalone library or in a federated analysis context where data remain in distinct (separate) servers
## Summary Information
* Author: [Open Risk](http://www.openriskmanagement.com)
* License: Apache 2.0
* Development website: [Github](https://github.com/open-risk/openLGD)
* Mathematical Documentation of LGD Models: [Open Risk Manual](https://www.openriskmanual.org/wiki/Loss_Given_Default_Models)
* Discussion: [Open Risk Commons](https://www.openriskcommons.org/c/openlgd/19)
**NB: This is an early alpha release. openLGD is still in active development**
## Introduction
openLGD aims to support the development of both expert based and statistical [LGD Models](https://www.openriskmanual.org/wiki/LGD%20Model)
### Standalone Mode
In *standalone mode* openLGD emulates a classic use case where, e.g., a financial institution or other credit provider aims to develop a risk quantification system on the basis of data it has in its possession. Use cases for the standalone mode are both as intended (standalone) LGD model framework system and as a validation framework for federated applications.
The standalone mode is illustrated via the script standalone_run.py
### Federated Mode
The federated mode essentially facilitates the development of a *generic* (pooled) LGD model that applies to a wide population (which is assumed homogeneous)
#### Getting started with the federated demo
* Clone the repo in a local linux environment
* Install the dependencies in a virtual environment
* Fire up a number of flask servers on different shells. Check the [Spawn Cluster Script](./spawn_cluster.sh) for how to export the environment. This will fire up several Xterms where server output is logged
* Run the [Controller](./federated_run.py) script to perform the demo calculation
#### Fabric based configuration
Going forward we'll use fabric and yaml to ease deployment. Check [Fabfile](./fabfile.py) for preliminary functionality
#### Dependencies
- The statistical model estimation is currently using scikit-learn / statstmodels components
- The model server is based on the python flask framework.
The complete dependency list in the [requirements file](./requirements.txt)
#### Startup of the model servers:
The demo Model Servers are python/flask based servers
- The model servers should start up on ports http://127.0.0.1:500X/ where X is the serial number
- You can check the server is live by pointing your browser to the port
- or by using curl from the console (curl -v http://127.0.0.1:500X/)
#### Model Server API endpoints:
The general structure of the simplified API is
* GET http://127.0.0.1:500X/ API Root, indicating the server is live
* GET http://127.0.0.1:500X/start URL to get initial locally estimated parameters (cold start)
* POST http://127.0.0.1:500X/update URL to post current averaged parameters (warm start)
## White Papers on Federated Risk Analysis
* [White Paper: Federated Credit Systems, Part I: Unbundling The Credit Provision Business Model](https://www.openriskmanagement.com/white_paper_federated_credit_part_i_systems_unbundling_the_credit_provision_business_model/)
* [White Paper: Federated Credit Systems, Part II: Techniques for Federated Data Analysis](https://www.openriskmanagement.com/white_paper_federated_credit_systems_part_ii_techniques_for_federated_data_analysis/)