Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model
This addresses fraud detection for credit card companies, but it is incremental as it applies an existing method to a specific domain.
The paper tackled credit card fraud detection in unbalanced datasets using the ARIMA model, which was fitted to regular spending behavior to identify deviations, and it showed better detection power than four benchmark anomaly detection models.
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.