AUTOMOBILE INSURANCE FRAUD DETECTION USING ENSEMBLE LEARNING MODELS
Navin Duwadi, Dr Bhoj Raj Ghimire · International Journal of Engineering Applied Sciences and Technology · 2024
Automobile insurance fraud is a universal problem that has negative effects on both insurance companies and policyholders. This research proposes a novel ensemble learning model to accurately detect potential fraudulent vehicle insurance claims. By leveraging advanced machine learning techniques and addressing the challenge of imbalanced data, our model aims to enhance fraud detection efficiency and reduce financial losses. Our approach combines a stacking ensemble learner with carefully selected base classifiers, meta-classifier and data pre-processing techniques. We evaluate the model's performance on a real-world dataset of insurance claims, demonstrating superior results compared to existing methods. Notably, our model achieves high accuracy, recall, precision and area under curves, ensuring comprehensive detection and minimizing false positives.