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A Machine Learning Algorithm for Money Laundering Detection in Bank Melli Iran | ||
Journal of Business Data Science Research | ||
دوره 1، شماره 1، دی 2021، صفحه 5-13 اصل مقاله (462.11 K) | ||
نوع مقاله: Original Article | ||
نویسندگان | ||
Mehdi Shakeri Behbahani1؛ Naser Khani* 2 | ||
1Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran. | ||
2Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran. | ||
چکیده | ||
In the study, different feature selection methods were initially studied to prevent and detect money laundering, and then a new method was developed and used in three stages for the selection of features effective in detecting money laundering using a cellular learning automata-based algorithm. In the first stage, the patterns were extracted using paired features through a complete graph. In the second stage, the extracted patterns were trained and classified on the basis of the impact rate of features using the cellular learning automata (CLA). Finally, in the third stage, the optimized feature was selected based on the impact rate of features. Selection of effective features using the proposed method improved the accuracy of data classification to detect money laundering. The Bank Melli Iran data set was utilized by entering into MATLAB to evaluate the proposed method and compare it with other methods. The results showed that the accuracy rate of classification in the proposed CLA method to detect money laundering was 94.19% and its runtime was 263.32 seconds. The proposed method was observed to have higher classification accuracy in detecting money laundering, as compared to the listed methods. | ||
کلیدواژهها | ||
Feature selection؛ cellular learning automata؛ machine learning؛ money laundering detection | ||
مراجع | ||
Adamatzky, A. (2018). Cellular Automata: A Volume in the Encyclopedia of Complexity and Systems Science. Springer. Afrash, M., & Khodamoradi, M. (2014). Use of data mining for anti-money laundering and fraud detection National Conference on Engineering Sciences, New Ideas, Tonekabon. Ansari Pirsaraie, Z., & Shahbahrami, A. (2014). The need to use money laundering detection systems in electronic banking. Trend (Economic Research Trend), 21(68), 179-211. Dastjerdi, A. V., & Buyya, R. (2016). Fog computing: Helping the Internet of Things realize its potential. Computer, 49(8), 112-116. Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello, C. A. C., & Herrera, F. (2019). Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation, 48, 220-250. Feynman, R. P. (1986). Quantum mechanical computers. Foundations of physics, 16(6), 507-531. Hall, J. S. (1996). Utility fog: The stuff that dreams are made of. Nanotechnology, 161-184. Ilyas, M., & Mahgoub, I. (2018). Smart Dust: Sensor network applications, architecture and design. CRC press. Jafferis, N. T., Helbling, E. F., Karpelson, M., & Wood, R. J. (2019). Untethered flight of an insect-sized flapping-wing microscale aerial vehicle. Nature, 570(7762), 491-495. Judy, J. W. (2001). Microelectromechanical systems (MEMS): fabrication, design and applications. Smart materials and Structures, 10(6), 1115. Khomami, M. M. D., Rezvanian, A., & Meybodi, M. R. (2018). A new cellular learning automata-based algorithm for community detection in complex social networks. Journal of computational science, 24, 413-426. Koller, D., & Sahami, M. (1996). Toward optimal feature selection. Kumar, D., & Arora, S. (2016). A hybrid approach using maximum entropy and bayesian learning for detecting delinquency in financial industry. International Journal of Knowledge-Based Organizations (IJKBO), 6(1), 60-73. Mousavirad, S., & Ebrahimpour-Komleh, H. (2014). Wrapper feature selection using discrete cuckoo optimization algorithm. International Journal of Mechatronics, Electrical, and Computer Engineering, 4(11), 709-721. Naheem, M. A. (2016). Money laundering: A primer for banking staff. International Journal of Disclosure and Governance, 13(2), 135-156. Niccolai, L., Bassetto, M., Quarta, A. A., & Mengali, G. (2019). A review of Smart Dust architecture, dynamics, and mission applications. Progress in Aerospace Sciences, 106, 1-14. Saghiri, A. M., & Meybodi, M. R. (2017). A closed asynchronous dynamic model of cellular learning automata and its application to peer-to-peer networks. Genetic Programming and Evolvable Machines, 18(3), 313-349. Sharifi, S. (2013). Money Laundering and Countermeasures 2nd International Conference on Management, Entrepreneurship and Economic Development, Qom, Iran. Wang, X., Yang, J., Teng, X., Xia, W., & Jensen, R. (2007). Feature selection based on rough sets and particle swarm optimization. Pattern recognition letters, 28(4), 459-471. Warneke, B., Last, M., Liebowitz, B., & Pister, K. S. (2001). Smart dust: Communicating with a cubic-millimeter computer. Computer, 34(1), 44-51. Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. In Feature extraction, construction and selection (pp. 117-136). Springer. | ||
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