论文标题
使用Arima模型在信用卡交易中的异常和欺诈检测
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model
论文作者
论文摘要
本文解决了使用Arima模型在不平衡数据集中无监督的信用卡欺诈检测方法的问题。 Arima模型适合客户的常规支出行为，如果出现某些偏差或差异，用于检测欺诈。我们的模型应用于信用卡数据集，并将其与4种异常检测方法进行比较，例如K-均值，盒子图，本地离群因素和隔离林。结果表明，与基准模型相比，Arima模型具有更好的检测功率。
This paper addresses the problem of unsupervised approach of credit card fraud detection in unbalanced dataset using the ARIMA model. The ARIMA model is fitted on the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card datasets and is compared to 4 anomaly detection approaches such as K-Means, Box-Plot, Local Outlier Factor and Isolation Forest. The results show that the ARIMA model presents a better detecting power than the benchmark models.
