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Explainable Fraud Detection for Few Labeled Time Series Data
Security and Communication Networks ( IF 1.968 ) Pub Date : 2021-06-14 , DOI: 10.1155/2021/9941464
Zhiwen Xiao 1 , Jianbin Jiao 1
Affiliation  

Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed method. Experimental results show that our proposed method achieves the similar predictive performance as the state-of-art method, while our method can generate clear causal explanations for a few labeled time series data. The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons for rejecting transactions so as to reduce fraud losses and management costs.

中文翻译:

少数标记时间序列数据的可解释欺诈检测

欺诈检测技术是保障金融安全的重要手段。有必要开发可解释的欺诈检测方法来表达交易参与者的重大因果关系。我们工作的主要贡献是在多实例学习(MIL)的框架中提出了一种可解释的分类方法,该方法在自训练 LSTM 模型中结合了 AP 聚类方法以获得清晰的解释。基于真实世界数据集和模拟数据集,我们进行了两项比较研究来评估所提出方法的有效性。实验结果表明,我们提出的方法实现了与最先进方法相似的预测性能,而我们的方法可以为一些标记的时间序列数据生成清晰的因果解释。
更新日期:2021-06-14
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