论文标题
不平衡数据集的异常检测模型
Anomaly Detection Model for Imbalanced Datasets
论文作者
论文摘要
本文提出了一种使用混合方法，将随机强度模型与交易中观察到的欺诈可能性相结合的方法来检测银行欺诈。这是一种动态的无监督方法，能够预测财务欺诈。金融交易的欺诈预测概率是动态强度的函数。在这种情况下，提出了Kalman滤波器方法来估计动态强度。与其他基于强度的模型相比，我们的方法论在金融数据集中的应用显示出更高的不平衡数据中的预测能力。
This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic intensities. In this context, the Kalman filter method is proposed to estimate the dynamic intensities. The application of our methodology to financial datasets shows a better predictive power in higher imbalanced data compared to other intensity-based models.
