Credit Risk Modeling

This project builds a Credit Behavior Score Model to predict the creditworthiness of an individual based on financial and behavioral data.
It uses machine learning models (LightGBM and MLP) to classify whether a customer will likely default or behave responsibly.

An interactive Streamlit web app is developed where the user can upload a CSV file (containing one customer's data) to get instant predictions.

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Project Overview

Credit Risk Modeling is critical for financial institutions like banks, NBFCs, and lending platforms to assess the probability that a borrower will default on loan obligations.
This project builds a behavior-based credit scoring system, where the focus is not just on static demographic attributes (like age or income) but also on dynamic behavioral patterns — such as repayment history, credit utilization, outstanding balances, etc.

The model uses supervised machine learning techniques to estimate the likelihood of default (target: good/bad behavior) based on historical financial behavior data.

Key technical points:

• Features:
  Include a mix of customer demographics, financial attributes (credit limit, utilization rates), and payment behaviors (payment delays, missed payments).

• Target:
  A binary classification where the model predicts if the customer's future behavior is "good" (no default) or "bad" (default risk).

• Modeling Approach:

  • LightGBM (Gradient Boosted Trees): Efficient for large datasets and handles feature interactions well.
  • Multi-Layer Perceptron (MLP): Captures non-linear relationships between customer features and risk.

• Real-world Relevance:

  • Enables credit risk scoring for loan approvals, credit limit adjustments, or early collection interventions.
  • Supports risk-based pricing: adjusting interest rates based on predicted borrower risk.

The final product is wrapped in an interactive Streamlit web app, allowing easy single-customer scoring through CSV file uploads.

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Repository Structure

• app.py — Main Streamlit app script.
• Credit_Score_Behaviour.ipynb — Notebook for model development and training.
• lightgbm_model.pkl — Pre-trained LightGBM model.
• mlp_model.pkl — Pre-trained MLP model.
• scaler.pkl — Scaler object used for input normalization.
• requirements.txt — List of required packages.

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Getting Started

Prerequisites

• Python 3.7 or above
• pip package manager

Installation

1. Clone the Repository

git clone https://github.com/Saumi18/Credit-Risk-Modeling.git
cd Credit-Risk-Modeling

2. (Optional) Create and Activate a Virtual Environment

python -m venv venv
# For Windows
venv\Scripts\activate
# For Mac/Linux
source venv/bin/activate

3. Install the Required Dependencies

pip install -r requirements.txt

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Running the Streamlit App

After installing dependencies:

streamlit run app.py

Then open your browser at:

http://localhost:8501/

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How to Use the App

1. Prepare a CSV File

   • Create a .csv file containing exactly one row representing the customer's features.
   • Ensure the column names match the expected input features used during model training.

   Example format:

   feature1	feature2	feature3	...
   value1	value2	value3	...

2. Upload the CSV File

   • In the Streamlit app, click Browse files and upload your customer's CSV file.

3. Select the Model

   • Choose either LightGBM or MLP from the model selection dropdown.

4. Predict

   • Click the Predict button to get the credit risk prediction.

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Technologies Used

• Python for scripting and modeling
• LightGBM and MLP for classification
• scikit-learn for preprocessing
• Streamlit for web app development
• pandas, numpy, matplotlib, seaborn for data analysis and visualization

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Contribution

Currently, external contributions are not open, but feedback and suggestions are welcome!

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