The project aims to solve the problem of inefficient and slow credit risk evaluation in financial lending systems by using machine learning-based credit scoring.
Currently, banks and financial institutions rely on traditional rule-based or manual evaluation methods, which leads to delayed loan approvals, human bias, and inaccurate risk assessment, affecting both lenders and borrowers.
BaazCredit provides an AI-powered credit risk scoring system that evaluates a user’s financial profile and predicts their creditworthiness.
- Uses machine learning models to predict loan default risk
- Generates a risk score based on user financial history
- Reduces human bias in decision-making
- Enables faster and more accurate loan approval process
This solution improves transparency, speed, and accuracy in credit decision systems.
- 📊 AI-based credit risk prediction
- ⚡ Real-time risk scoring system
- 📉 Risk classification (Low / Medium / High Risk)
- 🔐 User authentication system
- 🧠 Machine learning model integration
- 📈 Visual representation of risk results
- 🧾 Data-driven decision support system
Frontend: React.js, HTML, CSS, Tailwind CSS
Backend: Node.js / Flask / FastAPI
Database: MongoDB / MySQL
AI/ML: Scikit-learn, Pandas, NumPy, XGBoost / Logistic Regression
Visualization: Matplotlib / Seaborn
git clone <repo-link>
cd project-folder
# Frontend setup
cd frontend
npm install
npm run dev
# Backend setup
cd backend
npm install
node server.js
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