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This is a fullstack web application that combines a React frontend with a Flask backend to analyze user data and generate concise credit risk reports using AI.

credit-risk-analyzer/
├── frontend/              # React frontend
│   ├── public/
│   ├── src/
│   └── package.json
│
├── backend/               # Flask backend
│   ├── scripts/
        ├── app.py
│   └── requirements.txt
├── README.md
└── .gitignore

• Node.js (for frontend)
• Python 3.8+ but <=3.12(for backend)
• npm or yarn

cd frontend
npm install
npm start

Runs React app on http://localhost:3000

cd backend
python -m venv venv
source venv/bin/activate     # On Windows use venv\Scripts\activate
pip install -r requirements.txt
cd scripts
python app.py

Starts backend at http://localhost:5000

• 📥 Upload & process user JSON data
• 🧠 Generate AI-powered risk reports
• 📄 Export or display PDF report (backend)
• ⚛️ Interactive React frontend
• 📡 RESTful API integration

You can deploy this app using:

• Frontend: Vercel, Netlify
• Backend: Render, Railway, Heroku, AWS EC2

• Frontend: React, JavaScript, CSS
• Backend: Flask, Python, ReportLab (PDF), Gemini, Auth0
• Others: Git, REST APIs

• The system accepts raw user data files in CSV format
• These files contain various features
• The backend parses these files into a structured format (like Pandas DataFrames) to prepare for further processing.

• Unsupervised Learning:
  • Before training any model, the system performs clustering (e.g., using K-Means or DBSCAN) on historical or simulated credit datasets.
• Goal: Automatically assign a risk label to each record:
  • Low Risk, Medium Risk, High Risk
• These labels serve as a pseudo ground truth for training the neural network.

• A simple feedforward neural network (using PyTorch or TensorFlow) is trained using the features and the risk labels from the clustering step.
• Architecture:
  • Input layer → Dense layers with Dropouts → ReLU activation → Output layer (with softmax or sigmoid for classification)
• This model learns to predict the risk category based on new unseen data.

• When a user uploads their data:
  • It is preprocessed to match the format expected by the neural net (scaling, encoding, etc.).
  • The trained model runs inference to output a risk score or category (e.g., High Risk).
• These predictions are stored temporarily and used in the report generation step.

• The predicted results are passed to Google Gemini APIs (or any Gemini-powered LLM endpoint) along with the original user data.
• The LLM is prompted to:
  • Generate a concise, natural language explanation of the risk.
  • Suggest actionable financial tips or highlight any red flags.
• Output: A well-structured, human-readable report in text form.

• To secure report downloads:
  • Auth0 is integrated with the frontend and backend.
  • Users must log in using OAuth (e.g., Google, GitHub, etc.) before they can trigger a download.
• Backend checks for a valid JWT access token before allowing the request to proceed.
• Once verified, the system generates the PDF version of the report and displays it or makes it downloadable .

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