Card Fraud Detection by Inductive Learning and Evolutionary Algorithm

Many fraud analysis system has been in the hearts of their rule based engine to generate an alert suspicious behavior. The rules system is usually based on expert knowledge. Automatic rules of the goal were to use ever found cases of fraud and lawful use to search new patterns and rules to help distinguish between the two. In this paper, we proposed an evolutionary inductive learning from credit card transaction data found rules, combined with genetic algorithm and cover algorithm. Covering algorithm will be a separate-conquer method inductive rule learning. Genetic algorithm embedded in the main circuit of the covering algorithm for rule search. Focus on the selection of attributes and define derived attributions to catch up time-dependent fraudulent features. Measuring complex factors is to avoid the phenomenon of over fitting introduction. From the millions of data with billions of steps computational understanding of unknown concept in need of advanced software development technology to support the implementation of the algorithm in a reasonable execution time. The system has been applied in the real world of credit card transaction data to distinguish between legitimate fraudulent transactions