Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection

Haibing LI, Man-Leung WONG

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1 Citation (Scopus)


In this study, we propose a new approach based on Grammar-based Genetic Programming (GBGP), token competition, multi-objective optimization, and ensemble learning for solving Financial Fraud Detection (FFD) problems. Token competition is a niching technique to maintain diversity among individuals. It can be used to adjust the objective values of each individual, and the individuals with similar objective values but different meanings are separated. 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 FFD is obligatory, in order to prevent the destructive results caused by financial fraud. 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.
Original languageEnglish
Title of host publicationMetaheuristics for Finding Multiple Solutions
EditorsMike PREUSS, Michael G. EPITROPAKIS, Xiaodong LI, Jonathan E. FIELDSEND
PublisherSpringer, Cham
Number of pages27
ISBN (Electronic)9783030795535
ISBN (Print)9783030795528
Publication statusPublished - 23 Oct 2021

Publication series

NameMetaheuristics for Finding Multiple Solutions
ISSN (Print)1619-7127

Bibliographical note

Funding Information:
Acknowledgements This research is supported by the LEO Dr. David P. Chan Institute of Data Science and the General Research Fund LU310111 from the Research Grant Council of the Hong Kong Special Administrative Region.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Financial fraud detection
  • Grammar-based genetic programming
  • Multi-objective optimization
  • Token competition


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