Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network

Abstract

Due to the rapid progress of the e-commerce and online banking, use of credit cards has increased considerably leading to a large number of fraud incidents. In this paper, we have proposed a novel approach towards credit card fraud detection in which the fraud detection is done in three phases. The first phase does the initial user authentication and verification of card details. If the check is successfully cleared, then the transaction is passed to the next phase where fuzzy c-means clustering algorithm is applied to find out the normal usage patterns of credit card users based on their past activity. A suspicion score is calculated according to the extent of deviation from the normal patterns and thereby the transaction is classified as legitimate or suspicious or fraudulent. Once a transaction is found to be suspicious, neural network based learning mechanism is applied to determine whether it was actually a fraudulent activity or an occasional deviation by a genuine user. Extensive experimentation with stochastic models shows that the combined use of clustering technique along with learning helps in detecting fraudulent activities effectively while minimizing the generation of false alarms.