Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model

09/16/2020
by   Giulia Moschini, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset