Financial fraud is a serious problem that often produces destructive results in the world and it is exacerbating swiftly in many countries. It refers to many activities including credit card fraud, money laundering, insurance fraud, corporate fraud, etc. The major consequences of financial fraud are loss of billions of dollars each year, investor confidence and corporate reputation. Therefore, a research area called Financial Fraud Detection (FFD) is obligatory, in order to prevent the destructive results caused by financial fraud. In this study, we propose a new approach based on multi-objectives optimization, Genetic Programming (GP), grammars, and ensemble learning for solving FFD problems. We comprehensively compare the proposed approach with Logistic Regression, Neural Networks, Support Vector Machine, Bayesian Networks, Decision Trees, AdaBoost, Bagging and LogitBoost on four FFD datasets including two real-life datasets. The experimental results showed the effectiveness of the new approach. It outperforms existing data mining methods in different aspects. There are two major contributions of the study. First, it evaluates a number of existing data mining techniques on the given FFD problems. Second, it suggests a new approach for handling these far-reaching problems. Moreover, a novel ensemble learning method called Statistical Selection Learning is proposed.
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