Financial fraud is a criminal act, which violates the law, rules or policy to gain unauthorized financial benefit. The major consequences are loss of billions of dollars each year, investor confidence or corporate reputation. A study 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 method based on Grammar-based Genetic Programming (GBGP), multi-objectives optimization and ensemble learning for solving FFD problems. We comprehensively compare the proposed method with Logistic Regression (LR), Neural Networks (NNs), Support Vector Machine (SVM), Bayesian Networks (BNs), Decision Trees (DTs), AdaBoost, Bagging and LogitBoost on four FFD datasets. The experimental results showed the effectiveness of the new approach in the given FFD problems including two real-life problems. The major implications and significances of the study can concretely generalize for two points. First, it evaluates a number of data mining techniques by the given real-life classification problems. Second, it suggests a new method based on GBGP, NSGA-II and ensemble learning.
|Title of host publication||2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 1 Jan 2015|
Bibliographical notePaper presented at the 2015 IEEE Congress on Evolutionary Computation (CEC), 25-28 May 2015, Sendai, Japan.
ISBN of the source publication: 9781479974924