Financial fraud detection by using grammar-based multi-objective genetic programming with ensemble learning
Document Type
Conference paper
Source Publication
2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publication Date
1-1-2015
First Page
1113
Last Page
1120
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
DOI
10.1109/CEC.2015.7257014
Publisher Statement
Copyright © 2015 IEEE. Access to external full text or publisher's version may require subscription.
Additional Information
Paper presented at the 2015 IEEE Congress on Evolutionary Computation (CEC), 25-28 May 2015, Sendai, Japan.
ISBN of the source publication: 9781479974924
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Li, H., & Wong, M.-L. (2015). Financial fraud detection by using grammar-based multi-objective genetic programming with ensemble learning. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1113-1120). Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/CEC.2015.7257014