| 摘要(英) |
Due to the anonymity and decentralization of Bitcoin, many governments and regulatory agencies have been cautious about it. In the future, regulation may become stricter. The Financial Supervisory Commission of Taiwan also plans to establish a virtual currency regulatory authority in Taiwan in May 2023.
Compared with traditional currencies, financial institutions must comply with international regulations to ensure that they do not provide services to criminals and terrorists. They also need to continuously monitor financial transactions to detect suspicious activities. These financial institutions have many operational procedures to monitor and verify customer information to confirm the real identity of customers. Failure to detect illegal transactions will lead to serious consequences for financial institutions, warnings or fines will be given to relevant institutions depending on the severity of the situation. Therefore, most financial institutions use Anti-Money Laundering（AML）solutions for sanctions and watchlist filtering and screening to monitor every transaction within the financial network to ensure that no transaction can be used to do business with prohibited persons. Recently, the financial community and academia unanimously believe that machine learning may have a significant impact on monitoring.
Therefore, this study uses the Bitcoin abnormal transaction dataset on Kaggle to further explore various machine learning algorithms under the characteristics of Bitcoin anonymous transactions, including Random Forest, Logistic Regression, XGBoost, Gradient Boosting, and Support Vector Machine, etc., for the efficiency of abnormal transaction monitoring. At the same time, because the features of this dataset have been pre-processed, all feature names are anonymous, so it is hoped to select a feature set that has a more significant impact on abnormal transaction detection through data-driven methods.
The experimental results of this research show that the efficiency of the model established by the XGBoost algorithm is the best, followed by the Random Forest algorithm. In the feature selection experiment, the transaction features and aggregation features have the most significant impact on the efficiency of the model. |
| 參考文獻 |
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