https://github.com/aimaster-dev/default_loan_prediction
This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.
https://github.com/aimaster-dev/default_loan_prediction
automation banking-applications classification credit-risk explainability finance fintech flask fraud-detection lightgbm loan-prediction loan-prediction-analysis model-interpretability roc-auc shap sklearn vuejs xgboost
Last synced: 22 days ago
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This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.
- Host: GitHub
- URL: https://github.com/aimaster-dev/default_loan_prediction
- Owner: aimaster-dev
- Created: 2025-05-15T15:34:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-29T08:08:38.000Z (12 months ago)
- Last Synced: 2025-06-27T08:40:57.828Z (11 months ago)
- Topics: automation, banking-applications, classification, credit-risk, explainability, finance, fintech, flask, fraud-detection, lightgbm, loan-prediction, loan-prediction-analysis, model-interpretability, roc-auc, shap, sklearn, vuejs, xgboost
- Language: JavaScript
- Homepage:
- Size: 2.82 MB
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤖 Loan Default Prediction – Automating Bank Credit Processes Using AI
This project applies AI and machine learning to predict whether a customer is likely to default on a loan, enabling banks to make informed and automated credit decisions.
---
## 📊 Problem Statement
Banks often struggle to accurately assess credit risk and loan default probability. This project automates that process by using classification models to predict default based on applicant data.
---
## 🧠 Key Features
- Predicts loan defaults using historical banking data
- Compares multiple ML algorithms (Logistic Regression, Random Forest, XGBoost, etc.)
- Evaluates models via ROC-AUC, F1 score, Precision, and Recall
- Provides explainability with **SHAP** (SHapley Additive exPlanations)
- Visualization of feature importance and model decisions
- End-to-end pipeline from data preprocessing to model interpretation
---
## 🧰 Tech Stack
- **Python**
- **scikit-learn**
- **XGBoost**
- **SHAP**
- **pandas**, **matplotlib**, **seaborn**
---
## 🛠️ Setup Instructions
1. **Clone the Repository**
```bash
git clone https://github.com/aimaster-dev/default_loan_prediction.git
cd default_loan_prediction
````
2. **Install Dependencies**
```bash
pip install -r requirements.txt
```
3. **Run the Notebook**
Open `loan_default_prediction.ipynb` in Jupyter or run it using:
```bash
jupyter notebook loan_default_prediction.ipynb
```
---
## 📈 Model Evaluation
* **Best Model:** XGBoost
* **ROC-AUC:** High discriminative power
* **SHAP Values:** Used to interpret individual predictions and global feature impact
---
## 📌 Highlights
* Automated model selection and tuning
* Transparent credit risk scoring using SHAP
* Business-focused evaluation for banking applications
---
## 📎 Resources
* 📄 [Project Report (PDF)](https://github.com/aimaster-dev/default_loan_prediction/blob/main/Automating_bank_credit_processes_using_AI.pdf)
---
## 📜 License
MIT License. See `LICENSE` for details.
---
## 🙌 Acknowledgments
Thanks to aimaster-dev for sharing this impactful project in AI-driven finance.