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🚗 Insurance Fraud Detection Using Machine Learning

An AI-powered dashboard that detects fraudulent insurance claims using Machine Learning, Deep Learning, and Anomaly Detection.

This project demonstrates how data science can help insurance companies identify suspicious claims and reduce fraud losses.

📌 Project Overview

Insurance fraud costs companies billions every year. Detecting fraud manually is slow and inefficient.

This project builds an AI fraud detection system that:

  • Analyzes insurance claim data
  • Predicts if a claim is fraudulent
  • Detects anomalies in claim behavior
  • Visualizes fraud analytics in a dashboard

The application includes a Streamlit dashboard where users can input claim data or upload a dataset to detect fraud automatically.

⚙️ Technologies Used

  • Python
  • Pandas
  • Scikit-learn
  • TensorFlow / Keras
  • Streamlit
  • Plotly
  • Joblib

🧠 Machine Learning Models

This project uses multiple AI techniques:

Random Forest Classifier

Used for the main fraud detection model.

Deep Learning Model

A neural network that learns patterns in claim data.

Isolation Forest

Detects anomalies and suspicious patterns in claims.

📂 Project Structure

insurance-fraud-detection │ ├── dataset/ │ └── insurance_claims.csv │ ├── model/ │ ├── fraud_model.pkl │ ├── anomaly_model.pkl │ └── deep_fraud_model.h5 │ ├── train_model.py ├── app.py ├── requirements.txt └── README.md

📊 Features

✔ Fraud claim prediction
✔ Deep learning fraud scoring
✔ Anomaly detection
✔ Interactive dashboard
✔ CSV batch fraud detection
✔ Fraud analytics charts
✔ Dataset validation
✔ Download sample dataset

📁 Dataset Format

The model expects the following columns:

Age, Sex, VehiclePrice, AccidentArea, Fault, PastClaims

Example:

Age,Sex,VehiclePrice,AccidentArea,Fault,PastClaims 25,Male,20000,Urban,Policy Holder,1 40,Female,15000,Rural,Third Party,0 30,Male,22000,Urban,Policy Holder,2

🚀 Installation

Clone the repository:

git clone https://github.com/ASKHATDIGRASKAR/Insurance-Fraud-Detection-Using-Machine-Learning.git

Navigate to the project folder:

cd insurance-fraud-detection

Install dependencies:

pip install -r requirements.txt

🏋️ Train the Model

Run the training script:

python train_model.py

This will generate the trained models inside the model/ folder.

▶ Run the Dashboard

Start the Streamlit application:

streamlit run app.py

Open the dashboard in your browser:

http://localhost:8501

📊 Dashboard Preview

The dashboard allows users to:

  • Enter claim information
  • Predict fraud probability
  • Upload claim datasets
  • Detect high-risk claims
  • Visualize fraud analytics

🔮 Future Improvements

  • SHAP explainable AI
  • Fraud risk gauge visualization
  • Real-time fraud detection API
  • Larger fraud dataset training
  • Deployment using Docker or cloud services

🤝 Contributing

Contributions are welcome.

  1. Fork the repository
  2. Create a new branch
  3. Submit a pull request

🙎 Authors

Akshat Digraskar (0246AL231009) Akshay Parmar (0246AL231010) Alok Mishra (0246AL231011) Anand Vaidya (0246AL231012)

📜 License

This project is licensed under the MIT License.