Credit Risk Modeling of Cryptocurrency Market Using Machine Learning: Application to Money Laundering Detection in Bitcoin Transactions

Document Type : Original Article

Authors
Zahra Bozorgtabar Baei 1
reza aghajannashtaei 2
Mohammadhasan Gholizadeh 3
1 PhD student of Financial engineering, Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran
2 Department of Business Management, Rasht Branch, Islamic Azad University, Rasht, Iran
3 Department of Management, University of Guilan, Rasht, Iran
10.30495/jik.2025.23609
Abstract
The purpose of this research is to provide a deeper understanding of credit risk modeling and to evaluate the performance of machine learning and deep learning algorithms in detecting money laundering (as an aspect of credit risk) in Bitcoin transactions. For this purpose, six different machine learning algorithms, including artificial neural network (ANN), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), and two deep learning algorithms including deep belief network (DBN) ) and long short-term memory (LSTM) have been used. In addition, elliptical money laundering detection data related to Bitcoin transactions have been used in this research as a dataset used in machine learning methods. The statistical sample covers transaction data for the year 2021. Computational analysis was performed using R software (version 3.4.0) and MATLAB. The results showed that random forest, support vector machine (SVM) and DBN algorithms provided the best performance. Other algorithms, including LSTM, KNN, and ANN, also perform well, but their performance is lower compared to random forest, SVM, and DBN. Overall, this study highlights the potential of machine learning and deep learning algorithms in detecting money laundering in the Bitcoin network.
Keywords
Money laundering detection
machine learning
Bitcoin
deep learning
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