Hybrid Approach for Improvising Credit Card Fraud Detection Based on Collective Animal Behaviour and SVM

Abstract

The explosive growth of Information Technology in the last few decades has resulted in automation in every possible field. This has also led to electronic fund transfers and increased usage of credit cards and debit cards. Credit card fraud costs consumers and the financial industry billions of dollars annually. In this paper we propose a hybrid approach to credit card fraud detection, where a combination of supervised and unsupervised approaches was used to detect fraudulent transactions. This includes a behaviour based clustering approach where we use patterns from collective animal behaviours to detect the changes in the behaviour of credit card users to minimize the false positives. This approach also opens the avenue to predict the collective behaviours of highly organized crime groups involved in credit card fraud activities which as an option is not explored so far. © Springer-Verlag Berlin Heidelberg 2013.