The project consisted of providing an AI powered solution to the users for credit assessment of loans. The project covered various aspects from classical AI, considering various statistical models, to the modern day neural network.

The project is enriched with various credit modeling techniques, giving access to the user to choose one or any from them. It also takes care of the different data sources from which data can be fetched and has been fully incorporated to handle data coming from various sources like JSON/XML or SQL.

It is a RESTFUL API module written in django and Django Rest framework with the Java SDK and the fineract plugin.

Ensure you have the following installed in your system:

1. git
2. MySQL v5.7
3. python v3.6.8+

You can install python for your platform by following the python getting started guide.

To set up server locally you need to install all the requirements listed in requirements.txt. But first, you need to create and activate your project virtual environment by using the commands:

python -m venv env

For Linux or MacOS environment:

source env/bin/activate

For windows environment:

./env/bin/activate

You can then install project dependencies using the following command:

pip install -r requirements.txt

Once you have successfully installed all the dependencies, you need to run the database migrations. First, you need to ensure your database connection is properlty configured. Start by making sure you have the required version of MySQL installed. Alternatively, you can run the required version of the database server in a container, instead of having to install it, like this:

docker run --name mysql-5.7 -p 3306:3306 -e MYSQL_ROOT_PASSWORD=mysql -d mysql:5.7

and stop and destroy it like this:

docker rm -f mysql-5.7

Beware that this database container database keeps its state inside the container and not on the host filesystem. It is lost when you destroy (rm) this container. This is typically fine for development. See Caveats: Where to Store Data on the database container documentation re. how to make it persistent instead of ephemeral.

You can the run the required project migrations using the command:

python manage.py migrate

Start the application server by running the command:

python manage.py serve

The app will start at http://127.0.0.1:8000/

• You can access DRF browsable API at: http://127.0.0.1:8000/api/v1
• You can access swagger docs at: http://127.0.0.1:8000/api-docs/swagger-ui
• You can access redoc docs at: http://127.0.0.1:8000/api-docs/redoc

It is possible to easily run Fineract Credit Scorecard server using docker and docker-compose.

As Prerequisites, you must have docker and docker-compose installed on your machine; see Docker Install and Docker Compose Install.

Alternatively, you can also use Podman (e.g. via dnf install podman-docker), and Podman Compose (e.g. via pip3 install podman-compose) instead of Docker.

Now you can spin up your instance by:

    docker-compose build
    docker-compose up -d

Once you are done, you can stop the containers by issueing the command:

    docker-compose down

The docker-compose.yml will build the scorecard server container from the source based on the Dockerfile.

To generate the client SDKs

• First Refresh the openapi spec:

  python manage.py spectacular --file openapi.yml
  

• Then generate the SDKs:

  ./gradlew clean buildJavaSdk
  

client SDKs will be generated in build/generated directory

• Refresh the openapi spec:

  python manage.py spectacular --file openapi.yml
  

The fineract scorecard integration takes the approach of a plugin as it's done for Fineract Pentaho.

Follow Scorecard Plugin README to see how to run Fineract with the scorecard integration.

For vscode(Visual Studio Code) users, you can follow this guide to setup your ide for django project development

Want to file a bug, request a feature, contribute some code, or improve documentation? Excellent! Read up on our guidelines for contributing and then check out one of our issues. Make sure you follow the guidelines before sending a contribution!