数字货币交易所洗钱行为检测

数字货币交易中的洗钱行为区别于传统金融犯罪形态，传统反洗钱技术手段难以直接适用. 针对数字货币交易所面对的洗钱行为检测需求和检测难点，通过定义交易行为，构建了一个层次化加权的交易行为特征描述体系，提出了一个结合孤立点检测和小类簇检测的数字货币交易行为异常检测方法，实现从交易行为到交易用户的洗钱可疑程度的量化度量. 在真实数字货币交易所数据集上进行评估实验，结果显示，异常交易行为、可疑洗钱用户、显著性异常交易行为和隐蔽性异常交易行为的检测准确率分别为96.02%、95.05%、95.83%和95.81%，均优于基准算法. 同时，本文算法的特征体系能对检测结果做出有效解释，帮助数字货币交易所安全员快速开展后续调查和取证工作.

Money Laundering Detection for Cryptocurrency Transactions

Money laundering in cryptocurrency transactions is differentiated from traditional financial crimes due to its strong anonymity and decentralization. The existing anti-money laundering techniques cannot be directly applied to cryptocurrency transactions. Considering the traceability, interpretability, and measurability of money laundering crime forensics, this paper designs a four-stage money laundering detection approach: (1) defining a set of transactions of a user in a period as a transaction behavior; (2) constructing a set of features to characterize transaction behaviors; (3) adopting outlier detection and small cluster detection methods to find out loud and subtle anomalous transactions; (4) analyzing the suspicious score distributions of users and calculating a suspected-launderer value for each of them. To evaluate the performance of our proposed method, a real-world money laundering dataset is obtained and experimentally evaluated. The experiment results show that our approach obtains 96.02%, 95.05%, 95.83%, and 95.81% accuracy in terms of abnormal transaction behaviors, suspected money launderers, loud abnormal transactions, and subtle abnormal transactions, respectively, all better than benchmark algorithms. Moreover, the carefully-designed features of transaction behaviors can offer supportive interpretations for the detection results and help exchange security officers to carry on further investigations and crime forensics.