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Credit scoring models are used to approve or reject loan applications. In this tutorial we will build a real-time credit scoring system on AWS. |
When individuals apply for loans from banks and other credit providers, the decision to approve a loan application is often made through a statistical model. This model uses information about a customer to determine the likelihood that they will repay or default on a loan, in a process called credit scoring.
In this example, we will demonstrate how a real-time credit scoring system can be built using Feast and Scikit-Learn on AWS, using feature data from S3.
This real-time system accepts a loan request from a customer and responds within 100ms with a decision on whether their loan has been approved or rejected.
This end-to-end tutorial will take you through the following steps:
- Deploying S3 with Parquet as your primary data source, containing both loan features and zip code features
- Deploying Redshift as the interface Feast uses to build training datasets
- Registering your features with Feast and configuring DynamoDB for online serving
- Building a training dataset with Feast to train your credit scoring model
- Loading feature values from S3 into DynamoDB
- Making online predictions with your credit scoring model using features from DynamoDB
 View Source on Github |
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