Abstract
Among the high volume and frequency of credit card transactions in today’s world , the fraudulent ones must be flagged instantaneously. To establish its credibility the algorithm must do so with high sensitivity and minimum false alerts. We propose a light weight and easily re-trainable two stage semi-supervised approach to handle the problem. In the first phase we eliminate noisy data points (outliers) and extract core data points through a clustering technique. The extracted core points form the basis of training data. Set of core points are enhanced further to address the challenge of class imbalance (as fraudulent data is very less). Training set is used in the second phase to model the final classifier using k-D Tree. Experimental results on bench-marked dataset establish that the proposed model is well suited for real-time prediction of fraud transactions with high specificity.
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Lahiri, S., Misra, S., Saha, S.K., Mazumdar, C. (2022). Clustering-Based Semi-supervised Technique for Credit Card Fraud Detection. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_20
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