An Ensemble Feature Selection Model with Machine Learning Model for Detection of Fraudulent Motor Vehicle Insurance Claims
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Date
2025-05
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Kenyatta University
Abstract
Insurance companies are continuously inventing new competitive insurance products in
order to enlarge their market share. This has continuously created opportunities for
insurance fraud as well. Despite the insurance industry having extensive motor vehicle
policy data and claims information, fraudulent claims remain a significant challenge in
motor vehicle insurance. Proper analysis of this data can result in development of more
efficient methods for identifying fraudulent claims. The challenge lies on how to extract
valuable insights and knowledge from this data. This is because insurance datasets
inherently include noisy features or low-quality subsets of data. This study used feature
selection techniques to select relevant features from motor vehicle insurance claim
dataset. The selected features were then used in training machine learning model. The
machine learning model consisted of multiple machine learning algorithms whose
individual prediction results were combined by use of a voting method. This helped to
improve classification performance. Machine learning model’s performance with feature
selected dataset and with full dataset was then evaluated using recall, precision and F1-
score. The results indicated that the model trained with feature selected dataset performed
better than the model trained with full dataset attaining higher values in recall, precision
and F1-score. This indicated improved capability in minimizing false negative and
improved overall effectiveness in fraud detection. For feature work the model developed
for detecting fraudulent motor vehicle insurance claims can be enhanced by integrating
machine learning techniques with nature-inspired optimization algorithms. This will help
in better handling of extensive datasets and result to development of more rapid and
effective models for identifying false claims.
Description
A Research Project Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science in Computer Science in the School of Pure and Applied Sciences of Kenyatta University
Supervisor: