دانش سرمایه‌گذاری

دانش سرمایه‌گذاری

مدلسازی ریسک اعتباری بازار رمز ارزها با استفاده از یادگیری ماشین: کاربرد در تشخیص پولشویی در معاملات بیت‌کوین

نوع مقاله : مقاله پژوهشی

نویسندگان
زهرا بزرگ تبار بائی 1
رضا آقاجان نشتایی 2
محمدحسن قلی زاده 3
1 دانشجوی دکتری تخصصی مهندسی مالی، گروه مدیریت، واحد رشت، دانشگا ه آزاد اسلامی، رشت، ایران
2 گروه مدیریت بازرگانی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
3 گروه مدیریت، دانشگاه گیلان، رشت، ایران
10.30495/jik.2025.23609
چکیده
هدف از این پژوهش ارائه درک عمیق‌تری از مدل‌سازی ریسک اعتباری و ارزیابی عملکرد الگوریتم‌های یادگیری ماشین و یادگیری عمیق در تشخیص پولشویی (به عنوان جنبه‌ای از ریسک اعتباری) در تراکنش‌های بیت‌کوین است. برای این منظور از شش الگوریتم مختلف یادگیری ماشین، شامل شبکه عصبی مصنوعی (ANN)، جنگل تصادفی (RF)، K-نزدیکترین همسایه (KNN)، ماشین بردار پشتیبان (SVM)، و دو الگوریتم یادگیری عمیق شامل شبکه باور عمیق (DBN) و حافظه کوتاه‌مدت بلند (LSTM) استفاده شده است. به علاوه، داده‌های تشخیص پولشویی الیپتیک مربوط به معاملات بیت‌کوین در این پژوهش به عنوان مجموعه داده مورد استفاده در روش‌های یادگیری ماشین استفاده شده است. نمونه آماری داده‌های تراکنش‌های مربوط به سال 2021 میلادی را پوشش می‌دهد. تجزیه و تحلیل محاسباتی با استفاده از نرم افزار R (نسخه 3.4.0) و متلب انجام شده است. نتایج نشان داد الگوریتم‌های جنگل تصادفی، ماشین بردار پشتیبان (SVM) و DBN بهترین عملکرد را ارائه کردند. سایر الگوریتم‌ها، از جمله LSTM ، KNN، و ANN نیز عملکرد خوبی داشتند، اما عملکرد آنها در مقایسه با جنگل تصادفی، SVM و DBN پایین‌تر است. به طور کلی، این مطالعه پتانسیل یادگیری ماشین و الگوریتم های یادگیری عمیق را در تشخیص پولشویی در شبکه بیت کوین برجسته می‌کند.
کلیدواژه‌ها
تشخیص پولشویی
یادگیری ماشین
بیت‌کوین
یادگیری عمیق

عنوان مقاله English

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

نویسندگان English

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
چکیده English

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.

کلیدواژه‌ها English

Money laundering detection
machine learning
Bitcoin
deep learning
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