Welcome to the official repository for "Employing Machine Learning Techniques for Fraud Detection in Vehicle Insurance" – a natural language processing (NLP) and machine learning research project aimed at transforming the way insurance claims are analyzed and validated.
In the high-stakes world of vehicle insurance, fraud detection is more critical than ever. This project leverages cutting-edge Natural Language Processing (NLP) and Machine Learning (ML) techniques to intelligently assess and flag potentially fraudulent insurance claims.
Through this project, we explore how text-based claim descriptions, structured metadata, and behavioral insights can be harnessed using ML classifiers to enhance claims integrity.
- 🧠 Text Preprocessing & Feature Engineering: Tokenization, stopword removal, TF-IDF, and more.
- 🔍 Classification Models: Implementation of algorithms like Logistic Regression, Random Forest, and SVM to detect anomalies.
- 📊 Performance Evaluation: Accuracy, precision, recall, and F1-score metrics to assess model efficiency.
- 📚 Research-Driven Approach: Backed by academic analysis and real-world case studies.