This paper is a comparative analysis of different machine learning algorithms used to detect fraud claims of Automobile/vehicle Insurance claims dataset. In this paper large dataset of automobile insurance claims is used and three feature selection algorithms are applied to the dataset which will be used by the classification algorithms to detect the fraud claims. The Feature Selection algorithms used in this paper are Tree-Based Feature Selection Algorithm, L1-Based Feature Selection Algorithm and Univariate Feature Selection Algorithm and the classification algorithms are Random Forest (RF), Naive Bayes(NB), K-Nearest Neighbor(KNN) and Decision Tree(DT). These algorithms are compared on the basis of performance measures such as accuracy, precision, recall. The proposed model shows that Random Forest works well with respect to accuracy and precision and Decision Tree is the best with respect to recall.

Automobile Insurance,Machinelearning, Feature Selection Algorithms, Classification Algorithms

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