Clustering based fuzzy classification with a noise cluster in detecting fraud in insurance

Abstract

Fraud detection is one of the main issues in reducing the unsystematic risks in insurance business as its costs might reach to catastrophic amounts leading to higher loadings on reserves and premiums. Due to its cause of nature in diversity, fraud detection may require a wide range of factors and variables to be considered. To make logical relations between many factors and reveal their differences, estimate odds (or probabilities), and predict the fraud risk, scoring systems become an important aid. In this paper, we introduce a clustering-based fuzzy classification with a noise cluster (CBFCN) to identify the true state of a fraud. The approach proposed in this paper is based on fuzzy k-means clustering having a noise cluster (FKMN) and is a novel method for identifying outliers by achieving robust clustering. We integrate fuzzy theory to boost the prediction ability of machine learning (ML) approaches for a proper determination of the contributing features. The two critical features of the CBFCN method which are the membership values obtained from the FKMN clustering algorithm are implemented to capture the behavior of an existing structure better and detect the noise (extremes) in the dataset. Extensive analyses are made on two real datasets exposing different characteristics in their variables to demonstrate how CBFCN performs in detecting the fraud compared to the conventional approaches. Additionally, employing fuzzy approach to improve the ML performance is elaborated through the inclusion of noise clusters. The findings indicate that the suggested CBFCN models produce promising classification results in fraud detection in insurance claims occurrences.