Fraud Detection System

This project develops a machine learning-based classification system to detect fraudulent insurance claims. The system is designed to process customer and incident data, validate it, preprocess it, train a model on it, and deploy the model to cloud platforms for real-time fraud prediction.

Project Structure

• Data Ingestion and Validation: Validates incoming data files and organizes them based on integrity.
• Database Management: Stores validated data in a database for training and prediction.
• Model Training: Preprocesses data, clusters it using KMeans, and trains models with the highest AUC score for each cluster.
• Prediction: Predicts fraud likelihood on new data batches based on trained models.

Data Description

The dataset includes features such as:

• months_as_customer, age, policy_number, policy_bind_date
• policy_deductable, policy_annual_premium, incident_type
• total_claim_amount, injury_claim, property_claim
• Target Label: fraud_reported (Y or N)