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Securities fraud is a common worldwide problem, resulting in serious negative consequences to securities market each year. Securities Regulatory Commission from various countries has also attached great importance to the detection and prevention of securities fraud activities. Securities fraud is also increasing due to the rapid expansion of securities market in China. In accomplishing the task of securities fraud detection, China Securities Regulatory Commission (CSRC) could be facilitated in their work by using a number of data mining techniques. In this paper, we investigate the usefulness of Logistic regression model, Neural Networks (NNs), Sequential minimal optimization (SMO), Radial Basis Function (RBF) networks, Bayesian networks and Grammar Based Genet- ic Programming (GBGP) in the classification of the real, large and latest China Corporate Securities Fraud (CCSF) database. The six data mining techniques are compared in terms of their performances. As a result, we found GBGP outperforms others. This paper describes the GBGP in detail in solving the CCSF problem. In addition, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to generate synthetic minority class examples for the imbalanced CCSF dataset.

Knowledge Discovering in Corporate Securities Fraud by Using Grammar Based Genetic Programming