چکیده مقاله
This paper aims to compare three models of fraud detection in bodily injury ofcar insurance to select the best method with the least error Initially, properties were defined as fraud files, and then 120 files were selectedfrom the insurance company such that 20 of them were fraud cases Afterknowing the dataset, fraud files were predicted using the decision tree, supportvector machine SVM , and the k nearest neighbors KNN algorithm Comparing the results shows that in terms of total accuracy, KNN provides thebest result with k = 10 Therefore, it can detect fraudulent car insurance fileswith 100% accuracy and without error in any class
کلیدواژهها
نویسندگان
Sara Dadras
Jafar Soltani
Jafar Soltani
Sara Dadras
شیوه ارجاع
Dadras, Sara and Soltani, Jafar,1401,Identity Car Insurance Fraud Using Data Mining,The 29th National Conference and 10th International Conference on Insurance and Development (NCOID) with the main
ارائهشده در
مجموعه مقالات بیست و نهمین همایش ملی و دهمین همایش بین المللی بیمه و توسعه با موضوع «توسعه دانش بنیان صنعت بیمه»13 آذر 1401