Abstract
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.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, pp. 207–216. ACM (1993)
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)
Coello, C.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary algorithms for solving multi-objective problems. Springer (2007)
Cui, G., Wong, M.L., Wan, X.: Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment. J. Manag. Inf. Syst. 29(1), 341–373 (2012)
Cumming, D., Hou, W., Lee, E.: The role of financial analysts in deterring corporate fraud in China. SSRN Electron. J. (2011)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Eskandari, H., Geiger, C.D.: A fast pareto genetic algorithm approach for solving expensive multiobjective optimization problems. J. Heuristics 14(3), 203–241 (2008)
Goldberg, D., Richardson, J.: Genetic algorithms with sharing for multi-modal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49 (1987)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, USA (1989)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI, USA (1975)
Hopcroft, J.E.: Introduction to Automata Theory, Languages, and Computation, p. 3/E. Pearson Education India (2008)
Keane, M.A., Streeter, M.J., Mydlowec, W., Lanza, G., Yu, J.: Genetic Programming IV: Routine Human-competitive Machine Intelligence, vol. 5. Springer (2006)
Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)
Kou, Y., Lu, C.-T., Sirwongwattana, S., Huang, Y.-P.: Survey of fraud detection techniques. In: Proceedings of 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2. IEEE, pp. 749–754 (2004)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT press (1992)
Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X.: The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis. Support Syst. 50(3), 559–569 (2011)
Poli, R., Langdon, W., McPhee, N.F.: A Field Guide to Genetic Programming. LuLu Enterprises (2008)
Ponsich, A., Jaimes, A.L., Coello, C.A.C.: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans. Evol. Comput. 17(3), 321–344 (2013)
Ravisankar, P., Ravi, V., Raghava Rao, G., Bose, I.: Detection of financial statement fraud and feature selection using data mining techniques. Decis. Support Syst. 50(2), 491–500 (2011)
Syeda, M., Zhang, Y.-Q., Pan, Y.: Parallel granular neural networks for fast credit card fraud detection. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, vol. 1. IEEE, pp. 572–577 (2002)
Wong, M.L.: A flexible knowledge discovery system using genetic programming and logic grammars. Decis. Support Syst. 31(4), 405–428 (2001)
Wong, M.L., Leung, K.S.: Evolutionary program induction directed by logic grammars. Evol. Comput. 5(2), 143–180 (1997)
Wong, M.L., Leung, K.S.: Data Mining Using Grammar Based Genetic Programming and Applications. Kluwer Academic Publisher (2000)
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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.
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Li, H., Wong, ML. (2021). Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection. In: Preuss, M., Epitropakis, M.G., Li, X., Fieldsend, J.E. (eds) Metaheuristics for Finding Multiple Solutions. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-030-79553-5_11
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