Claims Reporting Automation System

A cloud‑native simulation of a real enterprise workflow: mainframe output → report generation (Excel/PDF) → cloud upload → ML-powered predictions → dashboard for download.

This project modernizes a legacy reporting pipeline using FastAPI, React, GCP Cloud Run, Cloud Storage, PostgreSQL, and Python automation — all built using mock data (fully safe and compliant).

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🚀 Features

🔹 End‑to‑End Automated Reporting

• Upload mock claim files (CSV/TXT)
• Parse, clean, and validate data
• Generate Excel + PDF reports
• Upload reports to Google Cloud Storage
• View and download reports from a modern UI

🤖 ML-Powered Claims Prediction

• Predict claim outcomes (Paid/Denied) for pending claims
• Random Forest classification model
• Probability scores and confidence levels
• Interactive dashboard with visual predictions
• Real-time processing via React frontend

🔹 Cloud‑Native Architecture

• Backend: FastAPI on Cloud Run
• Frontend: React dashboard with Tailwind CSS
• Storage: Cloud Storage buckets
• Database: Cloud SQL (PostgreSQL) or SQLite (default)
• Scheduled jobs via Cloud Scheduler

🔹 Enterprise‑Grade Modules

• Clean folder architecture
• ML model integration
• RESTful API design
• Error handling and validation
• CORS-enabled for frontend integration

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📸 Screenshots

ML Claims Prediction Dashboard

Interactive dashboard showing ML predictions for pending claims with probability scores and confidence levels.

API Docs

- Shows the /docs endpoint

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🧱 Architecture Overview

Client (React)
→ Backend API (FastAPI)
→ Data Processing (pandas)
→ Report Generators (Excel/PDF)
→ Cloud Storage (GCP)
→ Database (PostgreSQL)

High-Level Workflow

Upload File → Parse & Clean → Aggregate Data 
→ Excel/PDF Generation → Upload to GCP → Metadata Saved 
→ Dashboard Download

ML Prediction Flow:
Upload CSV → Feature Engineering → Train Model 
→ Predict Pending Claims → Display Results in Dashboard

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🛠️ Tech Stack

Frontend

• React
• Tailwind CSS
• Axios
• Vercel / Cloud Run deployment

Backend

• FastAPI
• Python (pandas, numpy, scikit-learn, openpyxl, reportlab)
• SQLAlchemy
• Pydantic
• Google Cloud Storage SDK
• Machine Learning (Random Forest Classifier)

Cloud

• Cloud Run
• Cloud Storage
• Cloud SQL
• Cloud Scheduler

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📁 Folder Structure

Claims-Reporting-Automation-System/
├── app/
│   ├── main.py                 # FastAPI application
│   ├── api/
│   │   └── reports.py         # API endpoints (upload, predict, list)
│   ├── ml/
│   │   └── claims_predictor.py # ML model for predictions
│   ├── models/
│   │   └── report.py          # SQLAlchemy Report model
│   ├── db/
│   │   ├── base.py            # Database base
│   │   └── session.py        # Database session
│   └── services/
│       ├── excel_generator.py # Excel/PDF report generation
│       ├── ml_service.py      # ML service wrapper
│       └── storage.py        # Google Cloud Storage
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   └── ClaimsPredictor.js  # ML prediction UI
│   │   ├── api/
│   │   │   └── reports.js         # API client
│   │   ├── App.js
│   │   └── index.js
│   ├── package.json
│   └── tailwind.config.js
├── sample_data/
│   └── claims_export_2025.txt # Sample claims data
├── requirements.txt            # Python dependencies
└── README.md

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🧪 Sample Mock Data

Sample data is located in sample_data/claims_export_2025.txt:

ClaimID,Status,Amount,Date,Type
C001,Paid,550.25,2025-01-11,Medical
C002,Denied,200.00,2025-01-14,Dental
C003,Pending,130.50,2025-01-17,Vision
C004,Paid,1250.75,2025-01-18,Medical
...

Required Columns:

• ClaimID: Unique identifier
• Status: One of "Paid", "Denied", or "Pending"
• Amount: Numeric claim amount
• Date: Date in YYYY-MM-DD format
• Type: Claim type (e.g., "Medical", "Dental", "Vision")

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🔌 API Endpoints

Reports

• POST /reports/upload - Upload CSV file, generate Excel/PDF reports, upload to GCS
• POST /reports/predict - Upload CSV file, get ML predictions for pending claims
• GET /reports - List all processed reports

System

• GET /health - Health check endpoint

Interactive API Documentation

• GET /docs - Swagger UI (FastAPI auto-generated docs)
• GET /redoc - ReDoc documentation

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📜 Environment Variables

Create .env file in project root:

# Database (optional - defaults to SQLite)
DATABASE_URL=postgresql://user:pass@host:port/db

# Google Cloud Storage (required for file uploads)
GCP_BUCKET_NAME=your-bucket-name

# Frontend API URL (optional)
REACT_APP_API_URL=http://localhost:8000

Note: See SETUP_ENV.md for detailed setup instructions.

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▶️ How to Run Locally

Prerequisites

• Python 3.11+ with virtual environment
• Node.js 16+ and npm
• (Optional) PostgreSQL for production-like setup

Backend Setup

1. Create and activate virtual environment:

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

2. Install dependencies:

   pip install -r requirements.txt

3. Configure environment variables:

   • Copy .env.example to .env (if exists)
   • Set DATABASE_URL (optional - defaults to SQLite)
   • Set GCP_BUCKET_NAME (required for file uploads)

4. Start the server:

   uvicorn app.main:app --reload

   Server runs at: http://localhost:8000

   • API Docs: http://localhost:8000/docs
   • Health Check: http://localhost:8000/health

Frontend Setup

1. Navigate to frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start development server:

   npm start

   Frontend runs at: http://localhost:3000

Quick Test

1. Start backend: uvicorn app.main:app --reload
2. Start frontend: cd frontend && npm start
3. Open http://localhost:3000
4. Upload sample_data/claims_export_2025.txt for ML predictions

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☁️ Deployment

Backend on Cloud Run

• Build Docker image
• Push to Container Registry
• Deploy with Cloud SQL + Cloud Storage permissions

Frontend

• Deploy to Vercel or Cloud Run

Scheduler

• Cloud Scheduler → calls /reports/generate on cron

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📚 Documentation

• ML Integration Guide - Complete guide for ML prediction feature
• Environment Setup - Detailed environment variable configuration
• Virtual Environment Guide - Python virtual environment setup

📌 Features & Capabilities

Current Features

✅ CSV/TXT file upload and validation
✅ Excel and PDF report generation
✅ Google Cloud Storage integration
✅ ML-powered claims prediction
✅ Interactive React dashboard
✅ RESTful API with auto-generated docs
✅ SQLite (default) or PostgreSQL support

Future Enhancements

• Model persistence (save/load trained models)
• Prediction history in database
• Batch processing for large files
• Export predictions as CSV
• Role-based access control
• Email notifications
• Additional file formats
• Multi-file parallel processing

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📄 License

MIT License

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🤝 Contributions

PRs and improvements are welcome!

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🧪 Testing the ML Prediction

1. Start both servers (backend and frontend)
2. Upload sample data:
   • Go to http://localhost:3000
   • Click "Select CSV/TXT File"
   • Choose sample_data/claims_export_2025.txt
   • Click "Predict Claims"
3. View results:
   • See summary statistics
   • Review predictions table with probabilities
   • Check denial risk scores

🔧 Troubleshooting

Common Issues

Import errors (sklearn, numpy):

pip install scikit-learn==1.3.2 numpy==1.26.4

CORS errors:

• Backend includes CORS middleware for localhost:3000
• Check app/main.py for CORS configuration

File not found:

• Ensure sample_data/claims_export_2025.txt exists
• Check file path in ML predictor script

Frontend connection errors:

• Verify backend is running on port 8000
• Check REACT_APP_API_URL in frontend .env

See ML_INTEGRATION_GUIDE.md for detailed troubleshooting.

⭐ Acknowledgment

This project is a modern cloud implementation inspired by the typical mainframe → reporting → SharePoint workflow used in enterprise environments, redesigned using modern engineering practices for learning and demonstration.

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Built with: FastAPI • React • Tailwind CSS • scikit-learn • pandas • PostgreSQL/SQLite • Google Cloud Storage