Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection

In this paper, a hybrid fuzzy clustering techniques using Salp Swarm Algorithm (SSA) is proposed. The proposed fuzzy clustering method is used to optimize the cluster centroids obtained as an under sampling method. The performance of the proposed fuzzy clustering method is compared with some well-known clustering algorithms to shows the superiority of the proposed clustering algorithm. In addition, a novel hybrid Automobile Insurance Fraud Detection System is proposed in which undersampling of the majority class is performed by using the proposed fuzzy clustering algorithm which eliminates the outliers from the majority class samples. The balanced dataset for automobile fraud detection obtained after undersampling undergoes classification. Different classifiers used for this purpose are Random Forest Classifier, Logistic Regression Classifier and XGBoost Classifier. The performance of each of the three classifiers is evaluated by considering different performance metrics such as sensitivity, accuracy and specificity. The proposed fuzzy clustering method along with XGBoost outperforms the other methods presented.