Real-time credit scoring 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.
​Real-time Credit Scoring Example​
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​

Tutorials - Previous

Fraud detection on GCP

Next - Tutorials

Driver stats on Snowflake

Last modified 2mo ago

Export as PDF

Copy link

Edit on GitHub

Contents

Real-time Credit Scoring Example

​Real-time Credit Scoring Example​

Real-time Credit Scoring Example