Wambu, Anthony MwitiAraka, Erik2025-10-142025-10-142024-04Wambu, A. M., & Araka, E. (2024). An ensemble feature selection model with machine learning model for detection of fraudulent motor vehicle insurance claims. TIJER: International Research Journal, 11(4), 283–292. https://www.tijer.orghttps://tijer.org/tijer/papers/TIJERTHE3029.pdfhttps://ir-library.ku.ac.ke/handle/123456789/31713ArticleInsurance 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. In motor vehicle insurance, fraudulent claims continue to be a big challenge despite the insurance industry having vast amounts of motor vehicle insurance policy data and claim’s data. Proper analysis of this data can help to develop a more efficient way of detecting fraudulent claims. The challenge is how to extract insightful information and knowledge from this data since by their nature insurance datasets contain noisy features or subsets of data which are of poor quality. In order to achieve an effective machine learning model, one needs to choose the right set of features of data in the pre-processing step. Including noisy and less important features has proven to affect the performance of most of the existing machine learning models being used in the insurance companies. With aid of proper and effective feature selection techniques machine learning models that uses only relevant features of data can be developed in order to detect fraudulent insurance claims effectively. These models can be employed in insurance industry to aid in detecting fraudulent motor vehicle insurance claims. This will result in reduction of loss adjustment expenses and also improve customer satisfaction. Although there exist several other methods and ways of data preprocessing this study employed an ensemble multiple filter feature selection method. This study involved development of motor vehicle fraud detection model using multiple algorithms whose results were combined by use of a voting method.enArticle