Assessing credit risk accurately is one of the most critical challenges in the financial sector. Poor credit decisions can lead to loan defaults, financial losses, and reduced trust in lending systems. This project tackles that challenge by building an end-to-end Credit Risk Scoring Service that predicts the likelihood of a customer defaulting on a loan based on their financial history.
The result is a practical, production-ready machine learning service that can be integrated into real-world lending workflows.
Project Overview
The Credit Risk Scoring Service is a machine learning–powered API that estimates a borrower’s probability of default. Using historical financial data sourced from Kaggle, the system trains a supervised learning model to classify and score loan applicants based on risk.
The project doesn’t stop at model training. It includes:
- A REST API for inference
- Containerization with Docker
- Cloud deployment using AWS Elastic Beanstalk
This makes it a complete ML service, not just a notebook experiment.
Key Features
1. Creditworthiness Prediction
At its core, the system predicts how likely a customer is to default on a loan. This enables lenders to make informed decisions on loan approvals, interest rates, or risk mitigation strategies.
2. XGBoost Model with Scikit-Learn
The model is built using XGBoost, a powerful gradient boosting algorithm known for its strong performance on structured financial data. Integrated via Scikit-Learn, it offers both accuracy and reliability for credit scoring tasks.
3. REST API for Model Serving
A Flask-based API exposes the trained model, allowing external systems to send customer data and receive real-time predictions. This design makes the model easy to integrate into web apps, dashboards, or internal banking tools.
4. Dockerized for Consistency
The entire application is containerized using Docker, ensuring consistent behavior across development, testing, and production environments.
5. Cloud Deployment with AWS Elastic Beanstalk
The service is configured for deployment on AWS Elastic Beanstalk, simplifying infrastructure management while enabling scalability and reliability.
Tech Stack
- Modeling: Scikit-Learn (XGBoost)
- API Framework: Flask
- Containerization: Docker
- Cloud Platform: AWS Elastic Beanstalk
- Dependency Management: Pipenv
- Data Source: Kaggle
This stack strikes a good balance between simplicity, performance, and production readiness.
Project Structure
The repository is clean and easy to follow:
-
train.py– Handles data processing and model training -
predict.py– Flask application for serving predictions -
Dockerfile– Defines the container build process -
Pipfile&Pipfile.lock– Manage Python dependencies
This separation of concerns makes the project easy to maintain and extend.
Running the Application
Using Docker
The fastest way to get started is with Docker. After building the image, the service can be run locally and accessed via the exposed API port.
Local Setup (Without Docker)
For development purposes, the app can also be run locally using Pipenv and Gunicorn, closely mimicking a production setup.
Deployment Workflow
The project uses AWS Elastic Beanstalk for deployment, making it straightforward to:
- Initialize the environment
- Deploy the Dockerized application
- Manage and terminate environments when needed
This setup reflects real-world ML deployment practices commonly used in fintech products.
Why This Project Matters
Credit risk modeling is a foundational problem in finance, and this project demonstrates how machine learning can be applied responsibly and practically to solve it. Beyond prediction accuracy, the project shows a strong understanding of:
- Model serving
- Containerization
- Cloud deployment
- Production workflows
It’s a solid example of how to move from data science experimentation to deployable ML services.
Final Thoughts
The Credit Risk Scoring Service is a well-rounded machine learning project that combines modeling, API development, and cloud deployment. It’s especially valuable as a portfolio project because it reflects real industry use cases in fintech and lending.
With further enhancements like model monitoring, explainability (e.g., SHAP values), or authentication, this service could easily evolve into a production-grade lending decision system.
A strong, practical project with clear real-world relevance.
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