Improving a Rule-based Fraud Detection System with Classification Based on Association Rule Mining

Abstract

Improving a Rule-based Fraud Detection System with Classification Based on Association Rule MiningThe detection of fraudulent insurance claims is a great challenge for insurance companies. Although the detection possibilities are getting better and better, fraudsters do not hesitate also using newer and more sophisticated methods. Apart from establishing new fraud detection systems, also the existing systems need to be updated and improved as best as possible. One common detection system is a rule-based expert system that checks predefined rules and gives alerts when certain conditions are met. Usually, the rules are treated separately and correlations within the rules are considered insufficiently. The work at hand describes how the classification based on association rule mining is used for improving such rule-based systems by bringing in relations between pairs of rules. The rule weights are determined through a genetic optimizer.