A Novel Multiobjective Approach for Detecting Money Laundering with a Neuro-Fuzzy Technique

• Published in: 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC) pp. 454 - 458
• Main Authors: Jamshidi, Mohammad Behdad, Gorjiankhanzad, Mohammadreza, Lalbakhsh, Ali, Roshani, Saeed
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2019
• Subjects: ANFIS; artificial intelligence; Artificial neural networks; Banking; Computational intelligence; Computational modeling; currency exchange; Law; Money laundering; Software
• DOI: 10.1109/ICNSC.2019.8743234

Using the computational inelegant methods in processing financial data is a practicable action to reduce a wide variety of crime in this domain. In this paper, a new intelligent multiobjective to recognize money laundering in banks and currency exchanges is presented. The introduced approach is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) which is set up by MATLAB software. The proposed method can replace conventional methods to detect the risk of money laundering in suspicious banking transaction. In addition, this approach can be used in banking systems as an online technique to analyze the data of customers' accounts. Also, the probability of money laundering's risk for each exchange is processed and monitored. One of the main advantages of the system is categorizing customers for different customers. The results illustrate the accuracy of this system in filtration of accounts infected by money laundering is acceptable.