Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic performance environments: a case study in fraud detection

Abstract

A significant problem in the application of rule-based expert systems has arisen in the area of re-engineering such systems to support changes in initial requirements. In dynamic performance environments, the rate of change is accelerated and the re-engineering problem becomes significantly more complex. One mechanism to respond to such dynamic changes is to utilize a cultural algorithm (CA). The CA provides self-adaptive capabilities which can generate the information necessary for the expert system to respond dynamically. To illustrate the approach, a fraud detection expert system was embedded inside a CA. To represent a dynamic performance environment, four different application objectives were used. The objectives were characterizing fraudulent claims, nonfraudulent claims, false positive claims, and false negative claims. The results indicate that a culturally enabled expert system can produce the information necessary to respond to dynamic performance environments.