Money Laundering Detection based on Data Mining and Classification using Deep Learning
Keywords:
Data mining, fraud detection, attraction of health tourists, deep learning, deep neural networksAbstract
In recent years, and with the rapid advancement of technology, an emerging phenomenon called electronic banking has emerged that affects the lives of all people. Along with the many benefits that electronic banking has brought to people, the field of fraud and money laundering has also entered this field, which helps fraud in banking transactions to be carried out very accurately and systematically in the field of electronic banking. What is important is a precise, fast and calculated confrontation with profiteers who move large amounts of money in this way and enter it into their personal accounts with the utmost intelligence.
In this article, while introducing data mining and its techniques, we analyze and discuss the measures taken in the field of fraud detection in various sectors, especially banking. After that, we introduce 3 types of the most widely used data mining algorithms and implement them on bank transactions in the CCFD benchmark dataset. Finally, after examining the performance of the algorithms, the best algorithm in terms of its performance in fraud detection is introduced.
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Copyright (c) 2025 Mehdi Shakeri Behbahani (Author); Mehdi Sadeghzadeh; Naser Khani, Akbar Nabiollahi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
