混合马尔科夫预测模型及其在反洗钱中的应用研究

反洗钱中的一个重要问题是预测可疑账户未来可能发生的交易。马尔科夫模型在股票、商品价格、市场占有率等经济领域的预测中具有广泛的应用，但单一的马尔科夫模型的预测准确性有待提高。提出一种结合数据挖掘中聚类、关联规则和低序马尔科夫模型的混合马尔科夫模型，并在模型的建立过程中基于置信度进行剪枝以降低时间复杂度，最后将该模型用于预测反洗钱领域中账户之间的交易。实验表明，该模型具有较高的预测准确性，并在预测准确性和时间复杂度两者之间取得了较好的平衡。

Hybrid Markov Prediction Model and Research of its Applications in Anti-money Laundering

An important problem in anti-money laundering is to predict the possible transactions conducted by suspicious accounts. Markov model has a wide range of applications in economic predictions such as stock, commodity prices,market share and so on. But the prediction accuracy of the single markov model remains to be improved. A hybrid Markov model jointing with clustering,association rule and low order Markov model was proposed. In the process of constructing the model, the confidence-based pruning was conducted to reduce the time complexity. Finally, the model was used to predict the transactions among accounts in anti-money laundering. hhe experimental results show that this modcl has high prediction accuracy and is a good tradeoff between the prediction accuracy and the time complexity.